Prevalence of Anderson-Fabry disease in male patients with late onset hypertrophic cardiomyopathy

Background-Although studies have suggested that "late-onset" hypertrophic cardiomyopathy (HCM) may be caused by sarcomeric protein gene mutations, the cause of HCM in the majority of patients is unknown. This study determined the prevalence of a potentially treatable cause of hypertrophy, Anderson-Fabry disease, in a HCM referral population.Methods and Results-Plasma alpha-galactosidase A (alpha-Gal) was measured in 79 men with HCM who were diagnosed at greater than or equal to40 years of age (52.9 +/- 7.7 years; range, 40-71 years) and in 74 men who were diagnosed at <40 years (25.9 +/- 9.2 years; range, 8-39 years). Five patients (6.3%) with late-onset disease and 1 patient (1.4%) diagnosed at <40 years had low alpha-Gal activity. Of these 6 patients, 3 had angina, 4 were in New York Heart Association class 2, 5 had palpitations, and 2 had a history of syncope. Hypertrophy was concentric in 5 patients and asymmetric in 1 patient. One patient had left ventricular outflow tract obstruction. All patients with low alpha-Gal activity had alpha-Gal gene mutations.Conclusion-Anderson-Fabry disease should be considered in all cases of unexplained hypertrophy. Its recognition is important given the advent of specific replacement enzyme therapy

Exploring patterns of recurrent melanoma in Northeast Scotland to inform the introduction a digital self-examination intervention

Peer reviewedPublisher PD

Sorting live stem cells based on Sox2 mRNA expression.

PMCID: PMC3507951This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.While cell sorting usually relies on cell-surface protein markers, molecular beacons (MBs) offer the potential to sort cells based on the presence of any expressed mRNA and in principle could be extremely useful to sort rare cell populations from primary isolates. We show here how stem cells can be purified from mixed cell populations by sorting based on MBs. Specifically, we designed molecular beacons targeting Sox2, a well-known stem cell marker for murine embryonic (mES) and neural stem cells (NSC). One of our designed molecular beacons displayed an increase in fluorescence compared to a nonspecific molecular beacon both in vitro and in vivo when tested in mES and NSCs. We sorted Sox2-MB(+)SSEA1(+) cells from a mixed population of 4-day retinoic acid-treated mES cells and effectively isolated live undifferentiated stem cells. Additionally, Sox2-MB(+) cells isolated from primary mouse brains were sorted and generated neurospheres with higher efficiency than Sox2-MB(-) cells. These results demonstrate the utility of MBs for stem cell sorting in an mRNA-specific manner

Probabilistic Clustering of Time-Evolving Distance Data

We present a novel probabilistic clustering model for objects that are represented via pairwise distances and observed at different time points. The proposed method utilizes the information given by adjacent time points to find the underlying cluster structure and obtain a smooth cluster evolution. This approach allows the number of objects and clusters to differ at every time point, and no identification on the identities of the objects is needed. Further, the model does not require the number of clusters being specified in advance -- they are instead determined automatically using a Dirichlet process prior. We validate our model on synthetic data showing that the proposed method is more accurate than state-of-the-art clustering methods. Finally, we use our dynamic clustering model to analyze and illustrate the evolution of brain cancer patients over time

Ab initio prediction of Boron compounds arising from Borozene: Structural and electronic properties

Structure and electronic properties of two unusual boron clusters obtained by fusion of borozene rings has been studied by means of first principles calculations, based on the generalized-gradient approximation of the density functional theory, and the semiempirical tight-binding method was used for the transport calculations. The role of disorder has also been considered with single vacancies and substitutional atoms. Results show that the pure boron clusters are topologically planar and characterized by (3c-2e) bonds, which can explain, together with the aromaticity (estimated by means of NICS), the remarkable cohesive energy values obtained. Such feature makes these systems competitive with the most stable boron clusters to date. On the contrary, the introduction of impurities compromises stability and planarity in both cases. The energy gap values indicate that these clusters possess a semiconducting character, while when the larger system is considered, zero-values of the density of states are found exclusively within the HOMO-LUMO gap. Electron transport calculations within the Landauer formalism confirm these indications, showing semiconductor-like low bias differential conductance for these stuctures. Differences and similarities with Carbon clusters are highlighted in the discussion.Comment: 10 pages, 2 tables, 5 figure

TLR7-mediated skin inflammation remotely triggers chemokine expression and leukocyte accumulation in the brain

Background: The relationship between the brain and the immune system has become increasingly topical as, although it is immune-specialised, the CNS is not free from the influences of the immune system. Recent data indicate that peripheral immune stimulation can significantly affect the CNS. But the mechanisms underpinning this relationship remain unclear. The standard approach to understanding this relationship has relied on systemic immune activation using bacterial components, finding that immune mediators, such as cytokines, can have a significant effect on brain function and behaviour. More rarely have studies used disease models that are representative of human disorders. Methods: Here we use a well-characterised animal model of psoriasis-like skin inflammation—imiquimod—to investigate the effects of tissue-specific peripheral inflammation on the brain. We used full genome array, flow cytometry analysis of immune cell infiltration, doublecortin staining for neural precursor cells and a behavioural read-out exploiting natural burrowing behaviour. Results: We found that a number of genes are upregulated in the brain following treatment, amongst which is a subset of inflammatory chemokines (CCL3, CCL5, CCL9, CXCL10, CXCL13, CXCL16 and CCR5). Strikingly, this model induced the infiltration of a number of immune cell subsets into the brain parenchyma, including T cells, NK cells and myeloid cells, along with a reduction in neurogenesis and a suppression of burrowing activity. Conclusions: These findings demonstrate that cutaneous, peripheral immune stimulation is associated with significant leukocyte infiltration into the brain and suggest that chemokines may be amongst the key mediators driving this response

Semiparametric Multivariate Accelerated Failure Time Model with Generalized Estimating Equations

The semiparametric accelerated failure time model is not as widely used as the Cox relative risk model mainly due to computational difficulties. Recent developments in least squares estimation and induced smoothing estimating equations provide promising tools to make the accelerate failure time models more attractive in practice. For semiparametric multivariate accelerated failure time models, we propose a generalized estimating equation approach to account for the multivariate dependence through working correlation structures. The marginal error distributions can be either identical as in sequential event settings or different as in parallel event settings. Some regression coefficients can be shared across margins as needed. The initial estimator is a rank-based estimator with Gehan's weight, but obtained from an induced smoothing approach with computation ease. The resulting estimator is consistent and asymptotically normal, with a variance estimated through a multiplier resampling method. In a simulation study, our estimator was up to three times as efficient as the initial estimator, especially with stronger multivariate dependence and heavier censoring percentage. Two real examples demonstrate the utility of the proposed method

Association of Over-The-Counter Pharmaceutical Sales with Influenza-Like-Illnesses to Patient Volume in an Urgent Care Setting

We studied the association between OTC pharmaceutical sales and volume of patients with influenza-like-illnesses (ILI) at an urgent care center over one year. OTC pharmaceutical sales explain 36% of the variance in the patient volume, and each standard deviation increase is associated with 4.7 more patient visits to the urgent care center (p<0.0001). Cross-correlation function analysis demonstrated that OTC pharmaceutical sales are significantly associated with patient volume during non-flu season (p<0.0001), but only the sales of cough and cold (p<0.0001) and thermometer (p<0.0001) categories were significant during flu season with a lag of two and one days, respectively. Our study is the first study to demonstrate and measure the relationship between OTC pharmaceutical sales and urgent care center patient volume, and presents strong evidence that OTC sales predict urgent care center patient volume year round. © 2013 Liu et al

Search for electromagnetic properties of the neutrinos at the LHC

Exclusive production of neutrinos via photon-photon fusion provides an excellent opportunity to probe electromagnetic properties of the neutrinos at the LHC. We explore the potential of processes pp-> p gamma gamma p -> p nu anti-nu p and pp -> p gamma gamma p -> p nu anti-nu Z p to probe neutrino-photon and neutrino-two photon couplings. We show that these reactions provide more than seven orders of magnitude improvement in neutrino-two photon couplings compared to LEP limits.Comment: 11 pages, 4 tables, New backgrounds have been adde

Multisensory causal inference in the brain

At any given moment, our brain processes multiple inputs from its different sensory modalities (vision, hearing, touch, etc.). In deciphering this array of sensory information, the brain has to solve two problems: (1) which of the inputs originate from the same object and should be integrated and (2) for the sensations originating from the same object, how best to integrate them. Recent behavioural studies suggest that the human brain solves these problems using optimal probabilistic inference, known as Bayesian causal inference. However, how and where the underlying computations are carried out in the brain have remained unknown. By combining neuroimaging-based decoding techniques and computational modelling of behavioural data, a new study now sheds light on how multisensory causal inference maps onto specific brain areas. The results suggest that the complexity of neural computations increases along the visual hierarchy and link specific components of the causal inference process with specific visual and parietal regions