30 research outputs found

    Patient-specific computational fluid dynamics-assessment of aortic hemodynamics in a spectrum of aortic valve pathologies.

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    OBJECTIVES: The complexity of aortic disease is not fully exposed by aortic dimensions alone, and morbidity or mortality can occur before intervention thresholds are met. Patient-specific computational fluid dynamics (CFD) were used to assess the effect of different aortic valve morphologies on velocity profiles, flow patterns, helicity, wall shear stress (WSS), and oscillatory shear index (OSI) in the thoracic aorta. METHODS: A total of 45 subjects were divided into 5 groups: volunteers, aortic regurgitation-tricuspid aortic valve (AR-TAV), aortic stenosis-tricuspid aortic valve (AS-TAV), aortic stenosis-bicuspid aortic valve right-left cusp fusion (BAV[RL]), and aortic stenosis-right-non cusp fusion (AS-BAV[RN]). Subjects underwent magnetic resonance angiography, with phase-contrast magnetic resonance imaging at the sino-tubular junction to define patient-specific inflow velocity profiles. Hemodynamic recordings were used alongside magnetic resonance imaging angiographic data to run patient-specific CFD. RESULTS: The BAV groups had larger mid-ascending aorta diameters (P < .05). Ascending aorta flow was more eccentric in BAV (flow asymmetry = 78.9% ± 6.5% for AS-BAV(RN), compared with 4.7% ± 2.1% for volunteers, P < .05). Helicity was greater in AS-BAV(RL) (P < .05). Mean WSS was elevated in AS groups, greatest in AS-BAV(RN) (37.1 ± 4.0 dyn/cm2, compared with 9.8 ± 5.4 for volunteers, P < .05). The greater curvature of the ascending aorta experienced highest WSS and lowest OSI in AS patients, most significant in AS-BAV(RN) (P < .05). CONCLUSIONS: BAV displays eccentric flow with high helicity. The presence of AS, particularly in BAV-RN, led to greater WSS and lower OSI in the greater curvature of the ascending aorta. Patient-specific CFD provides noninvasive functional assessment of the thoracic aorta, and may enable development of a personalized approach to diagnosis and management of aortic disease beyond traditional guidelines

    Individual differences in attributional style but not in interoceptive sensitivity, predict subjective estimates of action intention

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    The debate on the existence of free will is on-going. Seminal findings by Libet et al. (1983) demonstrate that subjective awareness of a voluntary urge to act (the W-judgment) occurs before action execution. Libet's paradigm requires participants to perform voluntary actions while watching a clock hand rotate. On response trials, participants make a retrospective judgment related to awareness of their urge to act.This research investigates the relationship between individual differences in performance on the Libet task and self-awareness. We examined the relationship between W-judgment, attributional style (AS; a measure of perceived control) and interoceptive sensitivity (IS; awareness of stimuli originating from one's body; e.g., heartbeats). Thirty participants completed the AS questionnaire (ASQ), a heartbeat estimation task (IS), and the Libet paradigm. The ASQ score significantly predicted performance on the Libet task, while IS did not - more negative ASQ scores indicated larger latency between W-judgment and action execution. A significant correlation was also observed between ASQ score and IS. This is the first research to report a relationship between W-judgment and AS and should inform the future use of electroencephalography (EEG) to investigate the relationship between AS, W-judgment and RP onset. Our findings raise questions surrounding the importance of one's perceived control in determining the point of conscious intention to act. Furthermore, we demonstrate possible negative implications associated with a longer period between conscious awareness and action execution. © 2014 Penton, Thierry and Davis

    Ophthalmic statistics note 11: logistic regression

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    Previous notes in this series have been concerned with the common situation in ophthalmic and other clinical fields of describing relationships between one or more ‘predictors’ (explanatory variables) and, usually, one outcome measure (response variable). A classic method used in deriving relationships between outcomes and predictors is linear regression analysis. Linear regression is a member of a family of techniques known as general linear models, which also include analysis of variance and analysis of covariance; the latter of which was covered in a previous Ophthalmic Statistics Not

    Ophthalmic statistics note 10: data transformations

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    Many statistical analyses in ophthalmic and other clinical fields are concerned with describing relationships between one or more ‘predictors’ (explanatory or independent variables) and usually one outcome measure (response or dependent variable). Our earlier statistical notes make reference to the fact that statistical techniques often make assumptions about data.1 ,2 Assumptions may relate to the outcome variable, to the predictor variable or indeed both; common assumptions are that data follow normal (Gaussian) distributions and that observations are independent. It is, of course, entirely possible to ignore such assumptions, but doing so is not good statistical practice and in medicine; poor statistical practice can impact negatively upon patients and the public
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