Un cas isolé de tuberculose appendiculaire

La tuberculose est une affection qui sévit à l’état endémique dans notre pays, elle demeure un problème majeur de santé publique .la tuberculose appendiculaire primitive est une affection très rare. Nous rapportons le cas d'un patient de sexe masculin âgé de 17 ans, admis au service pour une prise en charge d'une douleur de la fosse iliaque droite évoluant dans un contexte fébrile avec conservation de l’état général posant le diagnostic d'une appendicite aigue. Une appendicectomie a été réalisée ; le compte rendu anatomopathologique était en faveur d'une tuberculose appendiculaire isolée. Le patient a été mis sous traitement anti-bacillaire complémentaire pendant neuf mois avec une bonne évolution clinique. Le diagnostic de tuberculose doit être évoqué en premier surtout dans les pays d'endémie tuberculeuse

A Novel Explainable CNN Model for Screening COVID-19 on X-ray Images

Due to the rapid propagation characteristic of the Coronavirus (COVID-19) disease, manual diagnostic methods cannot handle the large number of infected individuals to prevent the spread of infection. Despite, new automated diagnostic methods have been brought on board, particularly methods based on artificial intelligence using different medical data such as X-ray imaging. Thoracic imaging, for example, produces several image types that can be processed and analyzed by machine and deep learning methods. X-ray imaging materials widely exist in most hospitals and health institutes since they are affordable compared to other imaging machines. Through this paper, we propose a novel Convolutional Neural Network (CNN) model (COV2Net) that can detect COVID-19 virus by analyzing the X-ray images of suspected patients. This model is trained on a dataset containing thousands of X-ray images collected from different sources. The model was tested and evaluated on an independent dataset. In order to approve the performance of the proposed model, three CNN models namely Mobile-Net, Residential Energy Services Network (Res-Net), and Visual Geometry Group 16 (VGG-16) have been implemented using transfer learning technique. This experiment consists of a multi-label classification task based on X-ray images for normal patients, patients infected by COVID-19 virus and other patients infected with pneumonia. This proposed model is empowered with Gradient-weighted Class Activation Mapping (Grad-CAM) and Grad-Cam++ techniques for a visual explanation and methodology debugging goal. The finding results show that the proposed model COV2Net outperforms the state-of-the-art methods

ISSN 2347-954X (Print) Unaccustomed Localizations of Hydatic Cyst Experience of the Service

Abstract: The hydatid cyst is a cosmopolitan parasitic infection that constitutes a problem of the public health in the zones of raising of the developing countries. We return the result of a retrospective survey of 64 cases of unaccustomed localizations of hydatid cysts collected in the general surgery service of the Avicenne military hospital on one period of 17years. The middle age of our patients is of 32years with extremes going from 9 at 66 years. The clinical symptom depended on the seat of the hydatid cyst and the medical imagery permitted to put the diagnosis in the majority of the cases. All patients have been operated and the operative gesture was the most conservative possible and it varied according to the seat and the character of the lesion. The operative mortality is hopeless and the morbidity is at the surroundings of 20%.The human Hydatid disease provoked by the larval shape of a tapeworm of the dog: the echinococcus granulosis. It is an anthropozoonose that rages in several countries from the world to the endemic state. The localizations of predilection are the liver and the lungs of which they represent 85% of the cases; but in practice all organ can be reached. We returned in this survey 64 cases of rare and unaccustomed localizations of hydatid cyst. In conclusion, the hydatid cyst constitutes a real public health problem. It is always necessary to think of it especially among subjects living in a country to elevated endemic and to ask for the necessary complementary exams to put the diagnosis and to avoid therapeutic mistakes. The eradication of this affection rests on the prophylaxis

Combining CNN and Grad-Cam for COVID-19 Disease Prediction and Visual Explanation

With daily increasing of suspected COVID-19 cases, the likelihood of the virus mutation increases also causing the appearance of virulent variants having a high level of replication. Automatic diagnosis methods of COVID-19 disease are very important in the medical community. An automatic diagnosis could be performed using machine and deep learning techniques to analyze and classify different lung x-ray images. Many research studies proposed automatic methods for detecting and predicting COVID-19 patients based on their clinical data. In the leak of valid X-Ray images for patients with COVID-19 datasets, several researchers proposed to use augmentation techniques to bypass this limitation. However, the obtained results by augmentation techniques are not efficient to be projected for the real world. In this paper, we propose a convolutional neural network (CNN)-based method to analyze and distinguish COVID-19 cases from other pneumonia and normal cases using the transfer learning technique. To help doctors easily interpret the results, a recent visual explanation method called Gradient-weighted Class Activation Mapping (Grad-CAM) is applied for each class. This technique is used in order to highlight the regions of interest on the x-ray image, so that, the model prediction result can be easily interpreted by the doctors. This method allows doctors to focus only on the important parts of the image and evaluate the efficiency of the concerned model. Three selected deep learning models namely VGG16, VGG19, and MobileNet, were used in the experiments with transfer learning technique. To bypass the limitation of the leak of lung X-Ray images of patients with COVID-19 disease, we propose to combine several different datasets in order to assemble a new dataset with sufficient real data to accomplish accurately the training step. The best results were obtained using the tuned VGG19 model with 96.97% accuracy, 100% precision, 100% F1-score, and 99% recall

Association between paraoxonase 1 (PON1) polymorphisms and the risk of acute coronary syndrome

<p>PON1 activity and genotype in Morrocan population</p

Association between Paraoxonase 1 (PON1) Polymorphisms and the Risk of Acute Coronary Syndrome in a North African Population

<div><p>The purpose of the present study was to investigate the distribution of PON1 Q192R and L55M polymorphisms and activities in a North African population and to determine their association with cardiovascular complications. The prevalence of the QQ, QR, RR, LL, LM, and MM genotypes in the study population was 55.4%, 34.09%, 9.83%, 41.97%, 48.20%, and 9.83% respectively. The Q, R, L, and M alleles had a gene frequency of 0.755, 0.245, 0.67, and 0.33, respectively. The PON1 192 RR genotype was significantly more prevalent among ACS patients than among healthy subjects. There was a 4.33-fold increase in the risk of ACS in subjects presenting the PON1 192 RR genotype compared to those with the QQ genotype (OR=4.33; 95% CI=1.27–17.7). There was a significantly different distribution of PON1 L55M in the ACS patient groups (UA, STEMI, NSTEMI). Moreover, individuals presenting the PON1 55MM genotype present a higher risk for ACS than those with LL genotype (OR=3.69; 95% CI=1.61–11.80). Paraoxonase activities were significantly lower in coronary patients than in healthy subjects. The decrease in PON1 activity was inversely correlated with the number of concomitant risk factors for CVD (r=0.57, p<0.0001). The results of the present study suggested that the PON1 R and M alleles may play a role in the pathogenesis of cardiac ischemia in our North African population and that a decrease in PON1 activity may be a valuable marker for monitoring the development of the atherosclerosis process and the associated cardiovascular complications.</p></div

HANDBOOK OF RESEARCH METHODS IN CLINICAL-PSYCHOLOGY - KENDALL,PC, BUTCHER,JN

<p>The PON1 L55M genotype was determined by RT-PCR. PON1 paraoxonase activity was determined by measuring paraoxon absorbance at 412 nm. Results are expressed as means ± SEM. ****p<0.0001 for comparison between ACS patients and healthy subjects with the same PON1 L55M polymorphism.</p

PON1 phenotypic distribution, activities, and oxidative stress markers in the healthy subjects and ACS patients.

<p>Values are means ± SEM, unless indicated otherwise. The Student’s <i>t-test</i> and χ2 test (for PON1 phenotypic distribution) were used. Significance was established by comparing the results from the ACS patients with those from the healthy subjects:</p><p>**p<0.01,</p><p>***p<0.001,</p><p>****p<0.0001.</p><p>PON1 phenotypic distribution, activities, and oxidative stress markers in the healthy subjects and ACS patients.</p

Demographic and clinical data of the healthy subjects and the ACS patients.

<p>Values are means ± SEM, unless indicated otherwise. The student’s t-test and χ² test (for sex) were used. Significance was established by comparing the results from the ACS patients with those from the healthy subjects:</p><p>** p<0.05,</p><p>*** p<0.001.</p><p>HDL-C (HDL-cholesterol), LDL-C (LDL-cholesterol), TC (total cholesterol), CRP (C reactive protein), TG (triglycerides)</p><p>Demographic and clinical data of the healthy subjects and the ACS patients.</p

Genotype and allele frequencies of the L55M polymorphism.

<p>Contribution of the PON1 L192M polymorphism to ACS was estimated by logistic regression for unmatched data to obtain odds ratios for the PON1 L192M polymorphisms. <b><i>Italics bold indicate the odds ratios adjusted for age</i>, <i>sex</i>, <i>BMI and HDL cholesterol</i>.</b></p><p>*(%) of the entire population (healthy subjects and ACS patients)</p><p>Genotype and allele frequencies of the L55M polymorphism.</p