Combination protein biomarkers predict multiple sclerosis diagnosis and outcomes

Establishing biomarkers to predict multiple sclerosis diagnosis and prognosis has been challenging using a single biomarker approach. We hypothesised that a combination of biomarkers would increase the accuracy of prediction models to differentiate multiple sclerosis from other neurological disorders and enhance prognostication for people with multiple sclerosis. We measured 24 fluid biomarkers in the blood and cerebrospinal fluid of 77 people with multiple sclerosis and 80 people with other neurological disorders, using ELISA or Single Molecule Array assays. Primary outcomes were multiple sclerosis versus any other diagnosis, time to first relapse, and time to disability milestone (Expanded Disability Status Scale 6), adjusted for age and sex. Multivariate prediction models were calculated using the area under the curve value for diagnostic prediction, and concordance statistics (the percentage of each pair of events that are correctly ordered in time for each of the Cox regression models) for prognostic predictions. Predictions using combinations of biomarkers were considerably better than single biomarker predictions. The combination of cerebrospinal fluid [chitinase-3-like-1 + TNF-receptor-1 + CD27] and serum [osteopontin + MCP-1] had an area under the curve of 0.97 for diagnosis of multiple sclerosis, compared to the best discriminative single marker in blood (osteopontin: area under the curve 0.84) and in cerebrospinal fluid (chitinase-3-like-1 area under the curve 0.84). Prediction for time to next relapse was optimal with a combination of cerebrospinal fluid[vitamin D binding protein + Factor I + C1inhibitor] + serum[Factor B + Interleukin-4 + C1inhibitor] (concordance 0.80), and time to Expanded Disability Status Scale 6 with cerebrospinal fluid [C9 + Neurofilament-light] + serum[chitinase-3-like-1 + CCL27 + vitamin D binding protein + C1inhibitor] (concordance 0.98). A combination of fluid biomarkers has a higher accuracy to differentiate multiple sclerosis from other neurological disorders and significantly improved the prediction of the development of sustained disability in multiple sclerosis. Serum models rivalled those of cerebrospinal fluid, holding promise for a non-invasive approach. The utility of our biomarker models can only be established by robust validation in different and varied cohorts

Thermal Properties of Graphene, Carbon Nanotubes and Nanostructured Carbon Materials

Recent years witnessed a rapid growth of interest of scientific and engineering communities to thermal properties of materials. Carbon allotropes and derivatives occupy a unique place in terms of their ability to conduct heat. The room-temperature thermal conductivity of carbon materials span an extraordinary large range - of over five orders of magnitude - from the lowest in amorphous carbons to the highest in graphene and carbon nanotubes. I review thermal and thermoelectric properties of carbon materials focusing on recent results for graphene, carbon nanotubes and nanostructured carbon materials with different degrees of disorder. A special attention is given to the unusual size dependence of heat conduction in two-dimensional crystals and, specifically, in graphene. I also describe prospects of applications of graphene and carbon materials for thermal management of electronics.Comment: Review Paper; 37 manuscript pages; 4 figures and 2 boxe

Cardiac disease in patients with mucopolysaccharidosis: presentation, diagnosis and management

The mucopolysaccharidoses (MPSs) are inherited lysosomal storage disorders caused by the absence of functional enzymes that contribute to the degradation of glycosaminoglycans (GAGs). The progressive systemic deposition of GAGs results in multi-organ system dysfunction that varies with the particular GAG deposited and the specific enzyme mutation(s) present. Cardiac involvement has been reported in all MPS syndromes and is a common and early feature, particularly for those with MPS I, II, and VI. Cardiac valve thickening, dysfunction (more severe for left-sided than for right-sided valves), and hypertrophy are commonly present; conduction abnormalities, coronary artery and other vascular involvement may also occur. Cardiac disease emerges silently and contributes significantly to early mortality

Thermal characterization of bulk and thin film materials using the mirage-method

The photothermal laser beam deflection (PTD) is used to measure the thermal properties of bulk solids and thin film materials. A complete theoretical treatment of the mirage-deflection in the case of a 4 layer-model (gas/film/substrate/backing) and a line heating source has been performed. The thermal properties can be obtained by fitting the theoretical expression to the experimental data. The experimental setup is described and experimental results for the thermal diffusivities of bulk solids over a range of 3 decades are presented. The errors caused by an incorrect assignment of fixed parameters in the non-linear least-square-fitting procedure are studied. A sensitivity study for a film-substrate-system is shown

Additional file 1 of Combination protein biomarkers predict multiple sclerosis diagnosis and outcomes

Additional file 1: Table S1. An overview of the assays used for this study. BDNF: brain-derived neurotrophic factor; CCL27: CC motif chemokine ligand 27; CRP: C-reactive protein; CXCL: C-X-C motif chemokine ligand; GFAP: glial fibrillary acidic protein; IL: interleukin; LIF: leukaemia inhibitory factor; MCP1: monocyte chemoattractant protein 1; TGF-β: transforming growth factor beta; TNFα: tumour necrosis factor alpha; TNFR1: tumour necrosis factor receptor 1; TCC: terminal complement complex, VDBP: vitamin D binding protein. Table S2. Rates of missing or imputed data in assay results. Table S3. Characteristics of 4 test/ train cohorts. Table S4. Comparison of CSF biomarkers between multiple sclerosis and non-multiple sclerosis groups. Mann–Whitney comparisons, corrected for multiple comparisons using Benjamini–Hochberg procedure. Table S5. Comparison of serum biomarkers between multiple sclerosis and non-multiple sclerosis groups Mann–Whitney comparisons corrected for multiple comparisons using Benjamini–Hochberg procedure. Table S6. A progression from single through combinations of multiple biomarkers to predict multiple sclerosis versus non-multiple sclerosis status. Table S7. Breakdown of the Train / Test results for the combined CSF & serum modelling of MS versus non-MS status. Mean AUC values were ordered from lowest to highest, and the optimum model was selected when addition of a further analyte resulted in an AUC increase < 0.01. Table S8. Sensitivity analysis: all biomarker concentrations were corrected for age and sex according to a linear model generated in control samples. A progression from single through combinations of multiple biomarkers to predict multiple sclerosis versus non-multiple sclerosis status. Table S9. Concordance of biomarkers in predicting time to next relapse in univariate analysis (adjusted for sex and age). Table S10. A progression from single through combinations of multiple biomarkers to predict time to relapse and time to disability, adjusted for age and sex. Table S11. Concordance of biomarkers in predicting time to EDSS 6 in univariate analysis (adjusted for sex, age and disease modifying therapy). Fig. S1. The plots show the 3 models below, run on 1000 random selection of (approx 75%) Train and (approx. 25%) Test data. The left hand plot is the range of AUC’s from the Train data when used also as Test and the right-hand plot shows the range of AUC’s when Test data are used as test