2,760 research outputs found

    Modelling the natural history of Huntington's disease progression.

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    BACKGROUND: The lack of reliable biomarkers to track disease progression is a major problem in clinical research of chronic neurological disorders. Using Huntington's disease (HD) as an example, we describe a novel approach to model HD and show that the progression of a neurological disorder can be predicted for individual patients. METHODS: Starting with an initial cohort of 343 patients with HD that we have followed since 1995, we used data from 68 patients that satisfied our filtering criteria to model disease progression, based on the Unified Huntington's Disease Rating Scale (UHDRS), a measure that is routinely used in HD clinics worldwide. RESULTS: Our model was validated by: (A) extrapolating our equation to model the age of disease onset, (B) testing it on a second patient data set by loosening our filtering criteria, (C) cross-validating with a repeated random subsampling approach and (D) holdout validating with the latest clinical assessment data from the same cohort of patients. With UHDRS scores from the past four clinical visits (over a minimum span of 2 years), our model predicts disease progression of individual patients over the next 2 years with an accuracy of 89-91%. We have also provided evidence that patients with similar baseline clinical profiles can exhibit very different trajectories of disease progression. CONCLUSIONS: This new model therefore has important implications for HD research, most obviously in the development of potential disease-modifying therapies. We believe that a similar approach can also be adapted to model disease progression in other chronic neurological disorders.This study was supported by the Cotswold Trust, the Rosetrees Trust, donations to the Huntington’s disease clinic in the John van Geest Centre for Brain Repair, and NIHR award of the Biomedical Research Centre - Cambridge University NHS Foundation Trust. This project was also supported by EPSRC through projects EP/I03210X/1 and EP/G066477/1.This article has been accepted for publication in Journal of Neurology, Neurosurgery, and Psychiatry, following peer review. The definitive copyedited, typeset version J Neurol Neurosurg Psychiatry doi:10.1136/jnnp-2014-308153 is available online at: http://jnnp.bmj.com/content/early/2014/12/16/jnnp-2014-308153.long

    Birthweight and risk markers for type 2 diabetes and cardiovascular disease in childhood: the Child Heart and Health Study in England (CHASE).

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    AIMS/HYPOTHESIS: Lower birthweight (a marker of fetal undernutrition) is associated with higher risks of type 2 diabetes and cardiovascular disease (CVD) and could explain ethnic differences in these diseases. We examined associations between birthweight and risk markers for diabetes and CVD in UK-resident white European, South Asian and black African-Caribbean children. METHODS: In a cross-sectional study of risk markers for diabetes and CVD in 9- to 10-year-old children of different ethnic origins, birthweight was obtained from health records and/or parental recall. Associations between birthweight and risk markers were estimated using multilevel linear regression to account for clustering in children from the same school. RESULTS: Key data were available for 3,744 (66%) singleton study participants. In analyses adjusted for age, sex and ethnicity, birthweight was inversely associated with serum urate and positively associated with systolic BP. After additional height adjustment, lower birthweight (per 100 g) was associated with higher serum urate (0.52%; 95% CI 0.38, 0.66), fasting serum insulin (0.41%; 95% CI 0.08, 0.74), HbA1c (0.04%; 95% CI 0.00, 0.08), plasma glucose (0.06%; 95% CI 0.02, 0.10) and serum triacylglycerol (0.30%; 95% CI 0.09, 0.51) but not with BP or blood cholesterol. Birthweight was lower among children of South Asian (231 g lower; 95% CI 183, 280) and black African-Caribbean origin (81 g lower; 95% CI 30, 132). However, adjustment for birthweight had no effect on ethnic differences in risk markers. CONCLUSIONS/INTERPRETATION: Birthweight was inversely associated with urate and with insulin and glycaemia after adjustment for current height. Lower birthweight does not appear to explain emerging ethnic difference in risk markers for diabetes

    Moving out of the shadows: accomplishing bisexual motherhood

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    Our qualitative study explored the ways in which bisexual mothers came to identify as such and how they structured their relationships and parenting within hetero-patriarchal society. The experiences of seven self-identified White bisexual women (aged from 28 to 56-years-old) from across England and the Republic of Ireland were investigated through semi-structured interviews. Participants’ children were aged 8 months to 28 years old at the time of their interviews. A thematic narrative analysis highlighted the following issues that participants had encountered in constructing their self-identity: prioritizing children; connecting and disconnecting with others and finessing self-definition; questioning societal relationship expectations. Nevertheless, participants varied considerably in how each of the themes identified were reflected in their lives, in particular depending upon each participant’s interpretation of her local social context. Both motherhood and self-identifying as bisexual gave a sense of meaning and purpose to participants’ life stories, although participants sometimes foregrounded their commitment to their children even at a personal cost to their bisexual identity. Using three different theoretical perspectives from feminist theory, queer theory and life course theory, the narratives analysed revealed ways in which bisexual motherhood not only had been influenced both intentionally and unintentionally by heteronormative expectations but also had directly and indirectly challenged these expectations

    Time-Frequency based Feature Selection for Discrimination of non stationary Biosignals.

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    This research proposes a generic methodology for dimensionality reduction upon time-frequency representations applied to the classification of different types of biosignals. The methodology directly deals with the highly redundant and irrelevant data contained in these representations, combining a first stage of irrelevant data removal by variable selection, with a second stage of redundancy reduction using methods based on linear transformations. The study addresses two techniques that provided a similar performance: the first one is based on the selection of a set of the most relevant time?frequency points, whereas the second one selects the most relevant frequency bands. The first methodology needs a lower quantity of components, leading to a lower feature space; but the second improves the capture of the time-varying dynamics of the signal, and therefore provides a more stable performance. In order to evaluate the generalization capabilities of the methodology proposed it has been applied to two types of biosignals with different kinds of non-stationary behaviors: electroencephalographic and phonocardiographic biosignals. Even when these two databases contain samples with different degrees of complexity and a wide variety of characterizing patterns, the results demonstrate a good accuracy for the detection of pathologies, over 98%.The results open the possibility to extrapolate the methodology to the study of other biosignals
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