Smoking cessation and the reduction of disability progression in Multiple Sclerosis: a cohort study

Background: Smoking is associated with a more severe disease course in people with multiple sclerosis (MS). The magnitude of effect of smoking cessation on MS progression is unknown. The aim of this study was to quantify the impact of smoking cessation on reaching MS disability milestones. Methods: This is a cross-sectional study with retrospective reports. A comprehensive smoking questionnaire was sent to 1270 patients with MS registered between 1994 and 2013 in the Nottingham University Hospital MS Clinics database. Demographic and clinical data were extracted from the clinical database. Cox proportional hazard regression was used to estimate effects of smoke-free years on the time to Expanded Disability Status Scale (EDSS) scores 4.0 and 6.0. MS Impact Scale 29 (MSIS-29) and Patient Determined Disease Steps (PDDS) were used to assess the physical and psychological impact of smoking. Results: Each ‘smoke-free year’ was associated with 0.96 (95% CI: 0.95 to 0.97) times decreased risk of reaching EDSS 4.0 and 0.97 (95%CI: 0.95 to 0.98) times decreased risk of reaching EDSS 6.0. Non-smokers showed a significantly lower level of disability in all the self-reported outcomes compared with current smokers. Conclusion: The reduction in the risk of disability progression after smoking cessation is significant and time-dependent. The earlier the patients quit, the stronger the reduction in the risk of reaching disability milestones. The quantitative estimates of the impact of smoking cessation on reaching disability milestones in MS can be used in interventional trials

Coordinate based random effect size meta-analysis of neuroimaging studies

Low power in neuroimaging studies can make them difficult to interpret, and Coordinate based meta-analysis (CBMA) may go some way to mitigating this issue. CBMA has been used in many analyses to detect where published functional MRI or voxel-based morphometry studies testing similar hypotheses report significant summary results (coordinates) consistently. Only the reported coordinates and possibly t statistics are analysed, and statistical significance of clusters is determined by coordinate density. Here a method of performing coordinate based random effect size meta-analysis and meta-regression is introduced. The algorithm (ClusterZ) analyses both coordinates and reported t statistic or Z score, standardised by the number of subjects. Statistical significance is determined not by coordinate density, but by a random effects meta-analyses of reported effects performed cluster-wise using standard statistical methods and taking account of censoring inherent in the published summary results. Type 1 error control is achieved using the false cluster discovery rate (FCDR), which is based on the false discovery rate. This controls both the family wise error rate under the null hypothesis that coordinates are randomly drawn from a standard stereotaxic space, and the proportion of significant clusters that are expected under the null. Such control is necessary to avoid propagating and even amplifying the very issues motivating the meta-analysis in the first place. ClusterZ is demonstrated on both numerically simulated data and on real data from reports of grey matter loss in multiple sclerosis (MS) and syndromes suggestive of MS, and of painful stimulus in healthy controls. The software implementation is available to download and use freely

Easy to interpret Coordinate Based Meta-Analysis of neuroimaging studies: Analysis of Brain Coordinates (ABC)

Background: Functional MRI and voxel-based morphometry are important in neuroscience. They are technically challenging with no globally optimal analysis method, and the multiple approaches have been shown to produce different results. It is useful to be able to meta-analyse results from such studies that tested a similar hypothesis potentially using different analysis methods. The aim is to identify replicable results and infer hypothesis specific effects. Coordinate based meta-analysis (CBMA) offers this, but the multiple algorithms can produce different results, making interpretation conditional on the algorithm. New method: Here a new model based CBMA algorithm, Analysis of Brain Coordinates (ABC), is presented. ABC aims to be simple to understand by avoiding empirical elements where possible and by using a simple to interpret statistical threshold, which relates to the primary aim of detecting replicable effects.Results: ABC is compared to both the most used and the most recently developed CBMA algorithms, by reproducing a published meta-analysis of localised grey matter changes in schizophrenia. There are some differences in results and the type of data that can be analysed, which are related to the algorithm specifics.Comparison to other methods: Compared to other algorithms ABC eliminates empirical elements where possible and uses a simple to interpret statistical threshold. Conclusions: There may be no optimal way to meta-analyse neuroimaging studies using CBMA. However, by eliminating some empirical elements and relating the statistical threshold directly to the aim of finding replicable effects, ABC makes the impact of the algorithm on any conclusion easier to understand

About some robustness and complexity properties of G-graphs networks

Given a finite group G and a set S ⊂ G, we consider the different cosets of each cyclic group ⟨s⟩ with s ∈ S. Then the G-graph Φ(G, S) associated with G and S can be defined as the intersection graph of all these cosets. These graphs were introduced in Bretto and Faisant (2005) as an alternative to Cayley graphs: they still have strong regular properties but a more flexible structure. We investigate here some of their robustness properties (connectivity and vertex/edge-transitivity) recognized as important issues in the domain of network design. In particular, we exhibit some cases where G-graphs are optimally connected, i.e. their edge and vertex-connectivity are both equal to the minimum degree. Our main result concerns the case of a G-graph associated with an abelian group and its canonical base S, which is shown to be optimally connected. We also provide a combinatorial characterization for this class as clique graphs of Cartesian products of complete graphs and we show that it can be recognized in polynomial time. These results motivate future researches in two main directions: revealing new classes of optimally connected G-graphs and investigating the complexity of their recognitio

Functional reorganisation in chronic pain and neural correlates of pain sensitisation

© 2016 Published by Elsevier Ltd. Maladaptive mechanisms of pain processing in chronic pain conditions (CP) are poorly understood. We used coordinate based meta-analysis of 266 fMRI pain studies to study functional brain reorganisation in CP and experimental models of hyperalgesia.The pattern of nociceptive brain activation was similar in CP, hyperalgesia and normalgesia in controls. However, elevated likelihood of activation was detected in the left putamen, left frontal gyrus and right insula in CP comparing stimuli of the most painful vs. other site. Meta-analysis of contrast maps showed no difference between CP, controls, mood conditions. In contrast, experimental hyperalgesia induced stronger activation in the bilateral insula, left cingulate and right frontal gyrus.Activation likelihood maps support a shared neural pain signature of cutaneous nociception in CP and controls. We also present a double dissociation between neural correlates of transient and persistent pain sensitisation with general increased activation intensity but unchanged pattern in experimental hyperalgesia and, by contrast, focally increased activation likelihood, but unchanged intensity, in CP when stimulated at the most painful body part

Vesicle origami and the influence of cholesterol on lipid packing

The artificial phospholipid Pad-PC-Pad was analyzed in 2D (monolayers at the air/water interface) and 3D (aqueous lipid dispersions) systems. In the gel phase, the two leaflets of a Pad-PC-Pad bilayer interdigitate completely, and the hydrophobic bilayer region has a thickness comparable to the length of a single phospholipid acyl chain. This leads to a stiff membrane with no spontaneous curvature. Forced into a vesicular structure, Pad-PC-Pad has faceted geometry, and in its extreme form, tetrahedral vesicles were found as predicted a decade ago. Above the main transition temperature, a noninterdigitated Lα phase with fluid chains has been observed. The addition of cholesterol leads to a slight decrease of the main transition temperature and a gradual decrease in the transition enthalpy until the transition vanishes at 40 mol % cholesterol in the mixture. Additionally, cholesterol pulls the chains apart, and a noninterdigitated gel phase is observed. In monolayers, cholesterol has an ordering effect on liquid-expanded phases and disorders condensed phases. The wavenumbers of the methylene stretching vibration indicate the formation of a liquid- ordered phase in mixtures with 40 mol % cholesterol

The future of multiple sclerosis treatments

Introduction. There are not many conditions in which the last few decades have brought such a major change in the landscape of treatments as is the case of multiple sclerosis (MS). A number of disease modifying treatments (DMTs) are presently available for the treatment of the inflammatory phase of this disabling disease; however, the need for treating neurodegeneration and halting the progression of disability is still unmet. Areas covered: In this paper we review the available information on existing and emerging DMTs and we discuss their place within the context of different treatment strategies in MS. Expert Commentary: The future of MS treatments should include the development of new treatment strategies tackling disease progression, together with a better understanding of the side-effects and the best sequential strategy of implementation of available and emerging drugs

Comorbidity in multiple sclerosis: its temporal relationships with disease onset and dose effect on mortality

© 2019 The Authors. European Journal of Neurology published by John Wiley & Sons Ltd on behalf of European Academy of Neurology. Background and purpose: We aimed to determine the burden of comorbidities at the time of diagnosis of multiple sclerosis (MS), the risk of developing new comorbidities after diagnosis and the effect of comorbidities on mortality in patients with MS. Methods: This study used data from 2526 patients with incident MS and 9980 age-, sex- and physician-matched controls without MS identified from the UK Clinical Practice Research Datalink. Results: Before the MS diagnosis, the adjusted odds ratio for the association between MS and a Charlson comorbidity index score of 1–2, 3–4 or ≥5 was 131 [95% confidence interval (CI), 1.17–1.47], 1.65 (95% CI, 1.20–2.26) or 3.26 (95% CI, 1.58–6.70), respectively. MS was associated with increased risks of cardiovascular and neurological/mental diseases. After diagnosis, the adjusted hazard ratio for the association between MS and an increased risk of developing comorbidities was 1.13 (95% CI, 1.00–1.29). The risk of developing any comorbidity in terms of neoplasms, musculoskeletal/connective tissue diseases or neurological/mental diseases was higher in MS. Patients with MS had a higher mortality risk compared with controls, with a hazard ratio of 2.29 (95% CI, 1.81–2.73) after adjusting for comorbidities. There was a dose effect of pre-existing comorbidities on mortality. Conclusions: Patients with MS have an increased risk of developing multiple comorbidities both before and after diagnosis and pre-existing comorbidities have an impact on survival

Epilepsy and associated mortality in patients with multiple sclerosis

Background and purpose: We aimed to determine the prevalence of epilepsy in patients with multiple sclerosis (MS) at diagnosis, the risk of developing epilepsy after the diagnosis of MS and the relative risk of mortality associated with epilepsy.Methods: We used the UK Clinical Practice Research Data‐link to identify 2526 patients with incident MS and 9980 age‐, sex‐ and index year‐matched non‐MS controls from 1997 to 2006. Logistic regression was used to estimate odds ratios [95% confidence interval (CI)] for epilepsy and Cox regression was used to estimate hazard ratios (HRs) (95% CI) for epilepsy and mortality.Results: Patients with incident MS were on average 45 years old and 70.9% were female. At diagnosis, the prevalence of epilepsy in patients with MS was 1.30% compared with 0.57% in non‐MS controls. At diagnosis, MS was associated with an adjusted odds ratio (95% CI) of 2.11 (1.36–3.27) for pre‐existing epilepsy. Among epilepsy‐free patients, the cumulative probabilities of developing epilepsy, first recorded within 10 years of the index date, were 2.77% for patients with MS and 0.90% for controls. MS was associated with an adjusted HR (95% CI) of 6.01 (2.94–12.29) for epilepsy. Among patients with MS, epilepsy was associated with an HR (95% CI) of 2.23 (1.02–4.84) for all‐cause mortality.Conclusions: This population‐based study found an increased prevalence of epilepsy in patients with MS at diagnosis when compared with non‐MS controls and the risk of developing epilepsy was also higher following the MS diagnosis. Patients with MS with epilepsy had a higher risk of mortality compared with those without