1,087 research outputs found
Clinical outcome of SARS-CoV-2 infection in 7 adults with Duchenne muscular dystrophy attending a specialist neuromuscular centre
Due to their frailty and cardiorespiratory compromise adults with DMD are considered extremely vulnerable and at high risk of severe infection should they contract COVID-19. We report 7 adults with DMD aged 17–26 years who tested positive on a nasopharyngeal PCR swab for SARS-CoV-2. Despite long term corticosteroid treatment, severe respiratory compromise requiring night-time ventilation and receiving treatment for moderate to severe cardiomyopathy, none of the patients developed moderate to severe symptoms; in fact two remained asymptomatic and two developed only anosmia and reduced sensation. The remaining three developed transient fever with or without sore throat, cough and runny nose. All recovered fully without complication and no patient required hospitalization
Spectral Graph Convolutions for Population-based Disease Prediction
Exploiting the wealth of imaging and non-imaging information for disease
prediction tasks requires models capable of representing, at the same time,
individual features as well as data associations between subjects from
potentially large populations. Graphs provide a natural framework for such
tasks, yet previous graph-based approaches focus on pairwise similarities
without modelling the subjects' individual characteristics and features. On the
other hand, relying solely on subject-specific imaging feature vectors fails to
model the interaction and similarity between subjects, which can reduce
performance. In this paper, we introduce the novel concept of Graph
Convolutional Networks (GCN) for brain analysis in populations, combining
imaging and non-imaging data. We represent populations as a sparse graph where
its vertices are associated with image-based feature vectors and the edges
encode phenotypic information. This structure was used to train a GCN model on
partially labelled graphs, aiming to infer the classes of unlabelled nodes from
the node features and pairwise associations between subjects. We demonstrate
the potential of the method on the challenging ADNI and ABIDE databases, as a
proof of concept of the benefit from integrating contextual information in
classification tasks. This has a clear impact on the quality of the
predictions, leading to 69.5% accuracy for ABIDE (outperforming the current
state of the art of 66.8%) and 77% for ADNI for prediction of MCI conversion,
significantly outperforming standard linear classifiers where only individual
features are considered.Comment: International Conference on Medical Image Computing and
Computer-Assisted Interventions (MICCAI) 201
Linear models of activation cascades: analytical solutions and coarse-graining of delayed signal transduction
Cellular signal transduction usually involves activation cascades, the
sequential activation of a series of proteins following the reception of an
input signal. Here we study the classic model of weakly activated cascades and
obtain analytical solutions for a variety of inputs. We show that in the
special but important case of optimal-gain cascades (i.e., when the
deactivation rates are identical) the downstream output of the cascade can be
represented exactly as a lumped nonlinear module containing an incomplete gamma
function with real parameters that depend on the rates and length of the
cascade, as well as parameters of the input signal. The expressions obtained
can be applied to the non-identical case when the deactivation rates are random
to capture the variability in the cascade outputs. We also show that cascades
can be rearranged so that blocks with similar rates can be lumped and
represented through our nonlinear modules. Our results can be used both to
represent cascades in computational models of differential equations and to fit
data efficiently, by reducing the number of equations and parameters involved.
In particular, the length of the cascade appears as a real-valued parameter and
can thus be fitted in the same manner as Hill coefficients. Finally, we show
how the obtained nonlinear modules can be used instead of delay differential
equations to model delays in signal transduction.Comment: 18 pages, 7 figure
Ozone therapy in management and prevention of dental caries- A Review
Dental caries is the irreversible microbial disease of teeth causing demineralization of inorganic and destruction of organic. It is of serious concern as it can lead to pain due to various pulpal and periapical pathologies. It is a tedious job to prevent this dental caries which is very common dental problem with each and everyone. With new concepts emerging in prevention and management of caries, Ozone therapy is tool to prevent and manage dental caries. The use of ozone (O3) gas as a therapy is skeptical due to unstable structure. The main beneficial effect of ozone is its antibacterial effect against various bacteria. These antibacterial effects are even attributed to the prevention and management of caries. This therapy is of controversy as some prove this to be less or no effective or some prove to be more effective. This article reviews various benefits of ozone therapy in prevention and management of caries and also discussion on controversies to it
Prediction of myelopathic level in cervical spondylotic myelopathy using diffusion tensor imaging
postprin
Anosognosia for hemiplegia as a tripartite disconnection syndrome
© 2019 Pacella et al. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.The syndrome of Anosognosia for Hemiplegia (AHP) can provide unique insights into the neurocognitive processes of motor awareness. Yet, prior studies have only explored predominately discreet lesions. Using advanced structural neuroimaging methods in 174 patients with a right-hemisphere stroke, we were able to identify three neural systems that contribute to AHP, when disconnected or directly damaged: the (i) premotor loop (ii) limbic system, and (iii) ventral attentional network. Our results suggest that human motor awareness is contingent on the joint contribution of these three systems.Peer reviewedFinal Published versio
Nonlinear spinor field in Bianchi type-I Universe filled with viscous fluid: numerical solutions
We consider a system of nonlinear spinor and a Bianchi type I gravitational
fields in presence of viscous fluid. The nonlinear term in the spinor field
Lagrangian is chosen to be , with being a self-coupling
constant and being a function of the invariants an constructed from
bilinear spinor forms and . Self-consistent solutions to the spinor and
BI gravitational field equations are obtained in terms of , where
is the volume scale of BI universe. System of equations for and \ve,
where \ve is the energy of the viscous fluid, is deduced. This system is
solved numerically for some special cases.Comment: 15 pages, 4 figure
Deriving a multi-subject functional-connectivity atlas to inform connectome estimation
MICCAI 2014 preprintInternational audienceThe estimation of functional connectivity structure from functional neuroimaging data is an important step toward understanding the mechanisms of various brain diseases and building relevant biomarkers. Yet, such inferences have to deal with the low signal-to-noise ratio and the paucity of the data. With at our disposal a steadily growing volume of publicly available neuroimaging data, it is however possible to improve the estimation procedures involved in connectome mapping. In this work, we propose a novel learning scheme for functional connectivity based on sparse Gaussian graphical models that aims at minimizing the bias induced by the regularization used in the estimation, by carefully separating the estimation of the model support from the coefficients. Moreover, our strategy makes it possible to include new data with a limited computational cost. We illustrate the physiological relevance of the learned prior, that can be identified as a functional connectivity atlas, based on an experiment on 46 subjects of the Human Connectome Dataset
Squeeze-and-Breathe Evolutionary Monte Carlo Optimisation with Local Search Acceleration and its application to parameter fitting
Motivation: Estimating parameters from data is a key stage of the modelling
process, particularly in biological systems where many parameters need to be
estimated from sparse and noisy data sets. Over the years, a variety of
heuristics have been proposed to solve this complex optimisation problem, with
good results in some cases yet with limitations in the biological setting.
Results: In this work, we develop an algorithm for model parameter fitting
that combines ideas from evolutionary algorithms, sequential Monte Carlo and
direct search optimisation. Our method performs well even when the order of
magnitude and/or the range of the parameters is unknown. The method refines
iteratively a sequence of parameter distributions through local optimisation
combined with partial resampling from a historical prior defined over the
support of all previous iterations. We exemplify our method with biological
models using both simulated and real experimental data and estimate the
parameters efficiently even in the absence of a priori knowledge about the
parameters.Comment: 15 Pages, 3 Figures, 6 Tables; Availability: Matlab code available
from the authors upon reques
GYG1 causing progressive limb girdle myopathy with onset during teenage years (polyglucosan body myopathy 2)
An 84-year-old lady with slowly progressive limb and axial muscle weakness with onset in her teens was referred for genetic investigations. Targeted next generation sequencing (NGS) revealed a homozygous mutation GYG1 in exon5:c.487delG:p.D163fs, confirming the diagnosis of Polyglucosan Body Myopathy 2 (PGBM2). Retrospective review of muscle pathology revealed a florid vacuolar myopathy with histochemical and ultrastructural features consistent with a polyglucosan storage myopathy. No cardiac symptoms were reported. Our case is consistent with the core phenotype of GYG1-related PGBM2 apart from an early onset of weakness without cardiac symptoms. The presence of α-amylase resistant PAS-positive material in skeletal muscle biopsy of patients with slowly progressive limb girdle muscle weakness should prompt the search for GYG1 mutations. This case highlights the combined role of muscle pathology and NGS in the molecular resolution of undiagnosed patients with undiagnosed neuromuscular conditions
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