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

A quantitative clinical pharmacology-based framework for model-informed vaccine development

Historically, vaccine development and dose optimization have followed mostly empirical approaches without clinical pharmacology and model-informed approaches playing a major role, in contrast to conventional drug development. This is attributed to the complex cascade of immunobiological mechanisms associated with vaccines and a lack of quantitative frameworks for extracting dose-exposure-efficacy-toxicity relationships. However, the Covid-19 pandemic highlighted the lack of sufficient immunogenicity due to suboptimal vaccine dosing regimens and the need for well-designed, model-informed clinical trials which enhance the probability of selection of optimal vaccine dosing regimens. In this perspective, we attempt to develop a quantitative clinical pharmacology-based approach that integrates vaccine dose-efficacy-toxicity across various stages of vaccine development into a unified framework that we term as model-informed vaccine dose-optimization and development (MIVD). We highlight scenarios where the adoption of MIVD approaches may have a strategic advantage compared to conventional practices for vaccines.Pharmacolog

Prediction of myelopathic level in cervical spondylotic myelopathy using diffusion tensor imaging

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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 $\lambda F$, with $\lambda$ being a self-coupling constant and $F$ being a function of the invariants $I$ an $J$ constructed from bilinear spinor forms $S$ and $P$. Self-consistent solutions to the spinor and BI gravitational field equations are obtained in terms of $\tau$, where $\tau$ is the volume scale of BI universe. System of equations for $\tau$ 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