Gradient elasto-plasticity with the generalised interpolation material point method.

The modelling of geomechanics problems can require a method that allows large deformations and non-linear material behaviour, in this respect the Generalised Material Point Method (GIMPM) is ideal. A fully implicit version of GIMPM has recently been developed for geomechanics problems and some aspects of its implementation are described here. An area that has received less attention in material point methods is that conventional analysis techniques constructed in terms of stress and strain are unable to resolve structural instabilities such as shear banding. This is because they do not contain any measure of the length of the microstructure of the material analysed, such as molecule size or grain structure. Gradient theories provide extensions of the classical equations with additional higher-order terms. The use of length scales makes it possible to model a finite thickness shear band which is not possible with traditional methods. Much work has been done on using gradient theories to include the effect of microstructure in the finite element method (and other numerical analysis techniques) however this yet to be combined with material point methods. In this paper the key equations that are required to extend the implicit GIMPM method to include gradient elasto-plasticity are detailed

Cerebrospinal fluid anti-myelin antibodies are related to magnetic resonance measures of disease activity in multiple sclerosis

0.001), together constituting 85% of all positive CSF samples. In contrast, elevated anti-myelin IgG antibody reactivity was present in a minority of IND patients (21%), marginally present in controls (5%) and absent in OND patients (0%). Most strikingly, anti-myelin IgG antibody reactivity was related to the number of T2 lesions (r = 0.31, p = 0.041) and gadolinium enhancing T1 lesions (r = 0.37, p = 0.016) on brain MRI in CIS and relapse onset MS patients. Conclusion: CSF anti-myelin IgG antibodies are promising specific biomarkers in CIS and relapse onset MS and correlate with MR measures of disease activit

Economic Issues in Funding and Supplying Public Sector Information

BACKGROUND: Cerebrospinal fluid (CSF) biomarkers are increasingly being used for diagnosis of Alzheimer's disease (AD). OBJECTIVE: We investigated the influence of CSF intralaboratory and interlaboratory variability on diagnostic CSF-based AD classification of subjects and identified causes of this variation. METHODS: We measured CSF amyloid-beta (Abeta) 1-42, total tau (t-tau), and phosphorylated tau (p-tau) by INNOTEST enzyme-linked-immunosorbent assays (ELISA) in a memory clinic population (n = 126). Samples were measured twice in a single or two laboratories that served as reference labs for CSF analyses in the Netherlands. Predefined cut-offs were used to classify CSF biomarkers as normal or abnormal/AD pattern. RESULTS: CSF intralaboratory variability was higher for Abeta1-42 than for t-tau and p-tau. Reanalysis led to a change in biomarker classification (normal vs. abnormal) of 26% of the subjects based on Abeta1-42, 10% based on t-tau, and 29% based on p-tau. The changes in absolute biomarker concentrations were paralleled by a similar change in levels of internal control samples between different assay lots. CSF interlaboratory variability was higher for p-tau than for Abeta1-42 and t-tau, and reanalysis led to a change in biomarker classification of 12% of the subjects based on Abeta1-42, 1% based on t-tau, and 22% based on p-tau. CONCLUSIONS: Intralaboratory and interlaboratory CSF variability frequently led to change in diagnostic CSF-based AD classification for Abeta1-42 and p-tau. Lot-to-lot variation was a major cause of intralaboratory variability. This will have implications for the use of these biomarkers in clinical practice

Deciphering protein secretion from the brain to Cerebrospinal Fluid for biomarker discovery

Cerebrospinal fluid (CSF) is an essential matrix for the discovery of neurological disease biomarkers. However, the high dynamic range of protein concentrations in CSF hinders the detection of the least abundant protein biomarkers by untargeted mass spectrometry. It is thus beneficial to gain a deeper understanding of the secretion processes within the brain. Here, we aim to explore if and how the secretion of brain proteins to the CSF can be predicted. By combining a curated CSF proteome and the brain elevated proteome of the Human Protein Atlas, brain proteins were classified as CSF or non-CSF secreted. A machine learning model was trained on a range of sequence-based features to differentiate between CSF and non-CSF groups and effectively predict the brain origin of proteins. The classification model achieves an area under the curve of 0.89 if using high confidence CSF proteins. The most important prediction features include the subcellular localization, signal peptides, and transmembrane regions. The classifier generalized well to the larger brain detected proteome and is able to correctly predict novel CSF proteins identified by affinity proteomics. In addition to elucidating the underlying mechanisms of protein secretion, the trained classification model can support biomarker candidate selection

Bioinformatics tools and data resources for assay development of fluid protein biomarkers

Fluid protein biomarkers are important tools in clinical research and health care to support diagnosis and to monitor patients. Especially within the field of dementia, novel biomarkers could address the current challenges of providing an early diagnosis and of selecting trial participants. While the great potential of fluid biomarkers is recognized, their implementation in routine clinical use has been slow. One major obstacle is the often unsuccessful translation of biomarker candidates from explorative high-throughput techniques to sensitive antibody-based immunoassays. In this review, we propose the incorporation of bioinformatics into the workflow of novel immunoassay development to overcome this bottleneck and thus facilitate the development of novel biomarkers towards clinical laboratory practice. Due to the rapid progress within the field of bioinformatics many freely available and easy-to-use tools and data resources exist which can aid the researcher at various stages. Current prediction methods and databases can support the selection of suitable biomarker candidates, as well as the choice of appropriate commercial affinity reagents. Additionally, we examine methods that can determine or predict the epitope - an antibody’s binding region on its antigen - and can help to make an informed choice on the immunogenic peptide used for novel antibody production. Selected use cases for biomarker candidates help illustrate the application and interpretation of the introduced tools

Injury markers predict time to dementia in subjects with MCI and amyloid pathology

OBJECTIVES: Alzheimer disease (AD) can now be diagnosed in subjects with mild cognitive impairment (MCI) using biomarkers. However, little is known about the rate of decline in those subjects. In this cohort study, we aimed to assess the conversion rate to dementia and identify prognostic markers in subjects with MCI and evidence of amyloid pathology. METHODS: We pooled subjects from the VU University Medical Center Alzheimer Center and the Development of Screening Guidelines and Criteria for Predementia Alzheimer's Disease (DESCRIPA) study. We included subjects with MCI, an abnormal level of β-amyloid(1-42) (Aβ(1-42)) in the CSF, and at least one diagnostic follow-up visit. We assessed the effect of APOE genotype, CSF total tau (t-tau) and tau phosphorylated at threonine 181 (p-tau) and hippocampal volume on time to AD-type dementia using Cox proportional hazards models and on decline on the Mini-Mental State Examination (MMSE) using linear mixed models. RESULTS: We included 110 subjects with MCI with abnormal CSF Aβ(1-42) and a mean MMSE score of 26.3 ± 2.8. During a mean follow-up of 2.2 ± 1.0 (range 0.4-5.0) years, 63 subjects (57%) progressed to AD-type dementia. Abnormal CSF t-tau (hazard ratio [HR] 2.3, 95% confidence interval [CI] 1.1-4.6, p = 0.03) and CSF p-tau (HR 3.5, 95% CI 1.3-9.2, p = 0.01) concentration and hippocampal atrophy (HR 2.5, 95% CI 1.1-5.6, p = 0.02) predicted time to dementia. For subjects with both abnormal t-tau concentration and hippocampal atrophy, HR was 7.3 (95% CI 1.0-55.9, p = 0.06). Furthermore, abnormal CSF t-tau and p-tau concentrations and hippocampal atrophy predicted decline in MMSE score. CONCLUSIONS: In subjects with MCI and evidence of amyloid pathology, the injury markers CSF t-tau and p-tau and hippocampal atrophy can predict further cognitive decline

Novel diagnostic cerebrospinal fluid biomarkers for pathologic subtypes of frontotemporal dementia identified by proteomics

Introduction: Reliable cerebrospinal fluid (CSF) biomarkers enabling identification of frontotemporal dementia (FTD) and its pathologic subtypes are lacking. Methods: Unbiased high-resolution mass spectrometry-based proteomics was applied on CSF of FTD patients with TAR DNA-binding protein 43 (TDP-43, FTD-TDP, n = 12) or tau pathology (FTD-tau, n = 8), and individuals with subjective memory complaints (SMC, n = 10). Validation was performed by applying enzyme-linked immunosorbent assay (ELISA) or enzymatic assays, when available, in a larger cohort (FTLD-TDP, n = 21, FTLD-tau, n = 10, SMC, n = 23) and in Alzheimer's disease (n = 20), dementia with Lewy bodies (DLB, n = 20), and vascular dementia (VaD, n = 18). Results: Of 1914 identified CSF proteins, 56 proteins were differentially regulated (fold change >1.2, P <.05) between the different patient groups: either between the two pathologic subtypes (10 proteins), or between at least one of these FTD subtypes and SMC (47 proteins). We confirmed the differential expression of YKL-40 by ELISA in a partly independent cohort. Furthermore, enzyme activity of catalase was decreased in FTD subtypes compared with SMC. Further validation in a larger cohort showed that the level of YKL-40 was twofold increased in both FTD pathologic subtypes compared with SMC and that the levels in FTLD-tau were higher compared to Alzheimer's dementia (AD), DLB, and VaD patients. Clinical validation furthermore showed that the catalase enzyme activity was decreased in the FTD subtypes compared to SMC, AD and DLB. Discussion: We identified promising CSF biomarkers for both FTD differential diagnosis and pathologic subtyping. YKL-40 and catalase enzyme activity should be validated further in similar pathology defined patient cohorts for their use for FTD diagnosis or treatment development