Comparative Epidemiology of Coronavirus Infections in humans and animals

Coronavirusesare a group of RNA viruses that cause diseases in mammals and birds. In humans and birds, they cause respiratory tract infections that can range from mild to lethal. Mild illnesses in humans include some cases of the common cold, which can also be caused by other viruses, predominantly rhinoviruses, while more lethal varieties cause Severe Acute Respiratory Syndrome (SARS), Middle East Respiratory Syndrome (MERS), or the current pandemic Coronavirus disease 2019 (COVID-19). In cows and pigs Coronaviruses cause diarrhea like the transmissible gastroenteritis virus (TGEV), while in young calves, the Bovine Coronavirus (BCV) causes severe profuse enteritis. In this review we will go over the microbiology of coronaviruses, their classifications and the different infections caused by it in animals and human

Lymphangiome kystique de l’arrière-cavité des épiploons

Le lymphangiome kystique de l'arrière-cavité des épiploons est une localisation exceptionnelle dont le  diagnostic est suspecté par la radiologie et confirmé par l'examen anatomo-pathologique. L'exérèse  chirurgicale constitue le traitement de choix. Key words: Lymphangiome kystique, arrière-cavité des épiploons, chirurgi

Thrombose veineuse mésentérique supérieure compliquant une appendicite méconnue

La thrombose veineuse mésentérique supérieure (TVMS) peut se présenter selon un mode aigu, subaigu ou chronique. Réputée rare, elle peut être primitive ou secondaire. Les étiologies chirurgicales les plus fréquemment identifiées de TVMS sont la diverticulite colique et l'appendicite aiguë. Les auteurs ont jugé utile de rapporter une observation de TVMS compliquant une appendicite refroidie par les antibiotiques, tout en insistant sur la latence clinique de telle pathologie rendant son diagnostic et son traitement plus difficile.Pan African Medical Journal 2013; 14:1

Les carcinomes de la thyroïde: profils épidémiologique, clinique et thérapeutique, à propos de 102 cas

Les carcinomes thyroïdiens sont des tumeurs malignes assez rares, représentant 1% des cancers. Ils sont généralement de bon pronostic, et présentent des aspects cliniques et évolutifs variés selon leur origine histologique. Notre travail est une étude rétrospective portant sur 102 cas de carcinomes de la thyroïde, colligés sur un nombre total de 811 gestes chirurgicaux thyroïdiens, au service d'oto-rhino-laryngologie et de chirurgie cervico-faciale de l'hôpital militaire Avicenne de Marrakech, sur une période de 8 ans, allant de janvier 2006 à décembre 2013.Les carcinomes thyroïdiens atteignent le sujet jeune avant l'âge de 50 ans, en particulier le sexe féminin. La tendance dans les pays en voie de développement, comme dans le monde entier est en croissance continue, ceci peut être expliqué par l'amélioration des outils d'imagerie et des moyens diagnostiques cytologiques et anatomo-pathologiques.Keywords: Carcinome thyroïdien, chirurgie, anatomo-pathologi

Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey

Background: SARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice. Methods: This is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure. Results: Nine hundred fifty-four surgeons completed the survey. The median length of practice was 10 years; 78.2% included were male with a median age of 37 years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout. There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management. Conclusions: There was a significant burnout among trainees. Almost all aspects of clinical and research activities were affected with a significant reduction in the volume of research, outpatient clinic visits, surgical procedures, on-call hours, and emergency cases hindering the training. Trial registration: The study was registered on clicaltrials.gov "NCT04433286" on 16/06/2020

Rank and Signed-Rank Tests for Random Coefficient Regression Model

In this paper, we propose nonparametric locally and asymptotically optimal tests for the problem of detecting randomness in the coefficient of a linear regression model (in the Le Cam and H´ajek sense). That is, the problem of testing the null hypothesis of a Standard Linear Regression (SLR) model against the alternative of a Random Coefficient Regression (RCR) model. A Local Asymptotic Normality (LAN) property, which allows for constructing locally asymptotically optimal tests, is therefore established for RCR models in the vicinity of SLR ones. Rank and signed-rank based versions of the optimal parametric tests are provided. These tests are optimal, most powerful and valid under a wide class of densities. A Monte-Carlo study confirms the performance of the proposed tests

Optimal Pseudo-Gaussian and Rank-Based Random Coefficient Detection in Multiple Regression

Random coefficient regression (RCR) models are the regression versions of random effects models in analysis of variance and panel data analysis. Optimal detection of the presence of random coefficients (equivalently, optimal testing of the hypothesis of constant regression coefficients) has been an open problem for many years. The simple regression case has been solved recently (Fihri et al. (2017)), and the multiple regression case is considered here. This problem poses several theoretical challenges (a)a nonstandard ULAN structure, with log-likelihood gradients vanishing at the null hypothesis; (b) a cone-shaped alternative under which traditional maximin-type optimality concepts are no longer adequate; (c) a matrix of nuisance parameters (the correlation structure of the random coefficients) that are not identified under the null but have a very significant impact on local powers. Inspired by Novikov (2011), we propose a new (local and asymptotic) concept of optimality for this problem, and, for specified error densities, derive the corresponding parametrically optimal procedures.A suitable modification of the Gaussian version of the latter is shown to remain valid under arbitrary densities with finite moments of order four, hence qualifies as a pseudo-Gaussian test. The asymptotic performances of those pseudo-Gaussian tests, however, are rather poor under skewed and heavy-tailed densities. We therefore also construct rank-based tests, possibly based on data-driven scores, the asymptotic relative efficiencies of which are remarkably high with respect to their pseudo-Gaussian counterparts.info:eu-repo/semantics/publishe

Parametrically and Semiparametrically Efficient Detection of Random Regression Coefficients

Locally asymptotically optimal (in the Hajek-Le Cam sense) pseudo-Gaussian and rank-based procedures for detecting randomness of coefficients in linear regression models are proposed. The parametric and semiparametric efficiency properties of those procedures are investigated. Their asymptotic relative efficiencies (with respect to the pseudo-Gaussian procedure) turns out to be be huge under heavy-tailed and skewed densities, stressing the importance of an adequate choice of scores. Simulations demonstrate the excellent finite-sample performances of a class of rank-based procedures based on data-driven scores.info:eu-repo/semantics/publishe

Etablissement d'un modèle de prédiction de la Bluetongue basé sur les données météorologiques et de télédétection, cas de la région Fès-Meknès

Bluetongue (BT) is an infectious, arthropod borne viral disease of domestic and wild ruminants. BT is a notifiable disease of huge socio-economic concern and of major importance in the international trade of animals and animal products. However, no study has yet been carried out to propose a tool to anticipate the occurrence of the disease. The objective of this study is to perform a mathematical model for predicting suitable areas for the appearance of bluetongue disease. This model will allow the monitoring and management of this animal disease. The creation of this model is based on epidemiological data collected in the field. These are combined with two other types of data: Remote Sensing (vegetation index, altitude) and climate (temperature, rainfall). The model performed is based on logistic regression. The assumption about its validity was examined by testing many combinations based on modeling and validation on data of 2006 and 2009 in Fès-Meknès region. The validation rates obtained are between 73 % and 80 %. This approach requires, in order to be effectively used as an early warning and risk management tool by animal health decision-makers, an efficient and real-time collection of epidemiological field data.  Keywords: Epidemiology, modeling, bluetongue, Remote Sensing, logistic regression, animal health, prediction.La fièvre catarrhale ovine (ou Bluetongue) est une maladie virale des ruminants domestiques et sauvages qui peut causer des pertes économiques énormes. Ces dernières années, elle a sérieusement impacté la production animale au Maroc. Étant donné qu’il s’agit d’une maladie vectorielle, il est possible de proposer un outil d’anticipation de l’apparition de la Bluetongue en localisant les sites les plus susceptibles d’accueillir le vecteur porteur de cette maladie.  Puisqu’aucune étude dans ce sens n’a été réalisée au niveau national, l’objectif de travail est donc d’établir un modèle mathématique de prédiction des zones favorables à l’apparition de la Bluetongue. Ce modèle aidera au suivi et la gestion de cette maladie animale. L’établissement de ce modèle est basé sur les données épidémiologiques recueillies sur le terrain combinées à deux autres types de données: de télédétection (l’indice de végétation, l’altitude) et climatiques (température, pluviométrie). Le modèle créé dans le cadre de ce travail se base sur la régression logistique. L’hypothèse concernant sa validité a été examinée en testant plusieurs combinaisons basées sur la modélisation et la validation sur des données de 2006 et 2009 de la région Fès-Meknès. L’étude a pu démontrer que la meilleure façon pour modéliser cette maladie est d’alimenter régulièrement le modèle dynamique par les données les plus récentes sur l’apparition de la maladie. Les taux de validation obtenus sont situés entre 73 % et 80 %. Dans la perspective d’améliorer ce travail, Il serait intéressant aussi d’étudier le sens de propagation de cette maladie en introduisant d’autres facteurs comme l’hydrographie, la direction et la vitesse du vent. Mots clés: Épidémiologie, Modélisation, Bluetongue, Télédétection, régression logistique, maladie animale, prédiction, Maroc. &nbsp