La dermatomyosite paranéoplasique révélant un carcinome indifférencié du nasopharynx: à propos d’un cas

La dermatomyosite (DM) est une maladie inflammatoire d'origine inconnue qui se manifeste sous forme de myopathie associée à lésions cutanées typiques. L'association DM et cancer est fréquente (18 a 32% selon les séries). Décrite pour la première fois par Stertz en 1916 en association avec un cancer gastrique. Tous les types histologiques et toutes les localisations de cancers observés danss la population générale peuvent être associés à la DM. Son association avec le carcinome nasopharyngé (NPC) est peu décrite et de l'ordre d'un cas pour 1000 cas de cancer nasopharyngé. Nous rapportons une observation de dermatomyosite révélant un cancer du nasopharynx localement avance.Pan African Medical Journal 2016; 2

Sensitivity of the electrocardiographic forward problem to the heart potential measuement noise and conductivity uncertainties

International audienceIn this work we are interested in quantifying the conductivity and epicardial potential boundary data uncertainties for the forward problem of electrocardiography (ECG). Indeed these input data are very important for the computation of the torso potential and consequently for the computation of the ECG. We use a stochastic approach for two dimensional torso geometry. We attribute probability density functions for the various source of randomness, and apply stochastic finite elements based on generalized polynomial chaos method. This work is the first step in order to quantify the uncertainties in inverse problem, which the goal is to complete the epicardial data. The efficiency of this approach to solve the forward ECG problem and the usability to quantify the effect of organs conductivity and epicardial boundary data uncertainties in the torso are demonstrated through a number of numerical simulations on a 2D computational mesh of the torso geometry

Maxillofacial metastasis from breast cancer

Metastatic tumors to paranasal sinuses are exclusively rare. In this paper, we report acase of breast  carcinoma metastasizing to the right maxilla. The metastasis occurred 5 years after radical mastectomy and presented as a primary sinonasalmass. The diagnosis was confirmed with histopathologic  andimmunohistochemical examination however the patient died before starting any specific treatment  because of tumor bleeding.Key words: Breast cancer, maxillofacial, metastasi

Sensitivity of the Electrocardiography Inverse Solution to the Torso Conductivity Uncertainties

Electrocardiography imaging (ECGI) is a new non invasive technology used for heart diagnosis. It allows to construct the electrical potential on the heart surface only from measurement on the body surface and some geometrical informations of the torso. The purpose of this work is twofold: First, we propose a new formulation to calculate the distribution of the electric potential on the heart, from measurements on the torso surface. Second, we study the influence of the errors and uncertainties on the conductivity parameters, on the ECGI solution. We use an optimal control formulation for the mathematical formulation of the problem with a stochastic diffusion equation as a constraint. The descretization is done using stochastic Galerkin method allowing to separate random and de-terministic variables. The optimal control problem is solved using a conjugate gradient method where the gradient of the cost function is computed with an ad-joint technique. The efficiency of this approach to solve the inverse problem and the usability to quantify the effect of conductivity uncertainties in the torso are demonstrated through a number of numerical simulations on a 2D geometrical model. Our results show that adding ±50% uncertainties in the fat conductivity does not alter the inverse solution, whereas adding ±50% uncertainties in the lung conductivity affects the reconstructed heart potential by almost 50%

Sensitivity of the electrocardiographic problem to multiple independent sources of uncertainty

This work investigates the effects of the inputs parameters uncertainties (organs conductivities, boundary data) on the problem of electrocardiography (ECG).These inputs are very important for the construction of the torso potential for the forward problem and to reconstruct the missing electric epicardial in the case of the inverse problem. We propose a new stochastic formulation allowing to combine both sources of errors. we formulate the forward and the inverse problem to a stochastic one by considering the inputs parameters as random fields and a stochastic optimal control formulation. In order to quantify multiple independent sources of uncertainties on the forward and inverse solutions, we attribute suitable probability density functions for each randomness source, and apply stochastic finite elements based on generalized polynomial chaos method. The efficiency of this approach to solve the forward and inverse ECG problem and the usability to quantify the effect of organs conductivity and epicardial boundary data uncertainties in the torso are demonstrated through a number of numerical simulations on a 2D computational mesh of a realistic torso geometry

Sensitivity of the electrocardiography inverse solution to the torso conductivity uncertainties

Electrocardiography imaging (ECGI) is a new non invasive technology used for heart diagnosis. It allows to construct the electrical potential on the heart surface only from measurement on the body surface and some geometrical informations of the torso. The purpose of this work is twofold: First, we propose a new formulation to calculate the distribution of the electric potential on the heart, from measurements on the torso surface. Second, we study the influence of the errors and uncertainties on the conductivity parameters, on the ECGI solution. We use an optimal control formulation for the mathematical formulation of the problem with a stochastic diffusion equation as a constraint. The descretization is done using stochastic Galerkin method allowing to separate random and deterministic variables. The optimal control problem is solved using a conjugate gradient method where the gradient of the cost function is computed with an adjoint technique. The efficiency of this approach to solve the inverse problem and the usability to quantify the effect of conductivity uncertainties in the torso are demonstrated through a number of numerical simulations on a 2D geometrical model