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Background and Aims Infectious complications are the second leading cause of death in hemodialysis patients. This population is particularly exposed to bacteremia, on the one hand, because of the vascular access necessary for hemodialysis, which is a gateway to the various micro-organisms, and on the other hand, factors of susceptibility to infections. Infective endocarditis (IE) is the cardiac endothelium infection associated with bacteremia. It is a rare complication but its mortality remains high especially in patients on chronic hemodialysis. The aim of this study was to determine the microbiological profile, clinical and-biological profile, characteristics in the ultrasound, therapeutic modalities, and prognosis of IE in hemodialysis. Method This is a retrospective descriptive study of including chronic hemodialysis patients, admitted in the Nephrology and Internal Medicine Department A of the Charles Nicolle Hospital in Tunis for an IE during the period from 1973 to 2018. We used the modified Duke criteria to confirm the diagnosis of IE. Results Nineteen patients were included, including 12 men and 7 women (gender ratio=1.7). The average age was 49.1 years [29-66 years]. Seven of them (37%) were known to have a valvular disease, two of them had a double mitro-aortic valve replacement. Six of them (32%) were diabetic and two patients (11%) were on immunosuppressive therapy. The vascular access initially used for HD were arteriovenous fistula in 9 cases (47%), internal jugular catheter in 3 cases (16%), subclavian catheter in 1 case (5%), Canaud catheter in 3 cases (16%), and 2 patients were dialyzed by femoral catheter (11%). Clinically, all patients had an altered general condition, fever was present in 14 cases (74%) and a heart murmur in 10 cases (53%). Blood cultures were positive in 14 cases (74%). The isolated germs were Staphylococcus Aureus in 8 cases, Staphylococcus epidermidis in 4 cases, Pseudomonas aerogenosa in 3 cases, Enterobacterium in 1 case, enterococcus faecalis in 1 case, and Klebsielle oxytoca in one patient. On cardiac ultrasound, mitral valve damage was found in 10 patients, aortic sigmoid in 4 patients and tricuspid valve in 3 patients. The treatment included appropriate antibiotic therapy in all cases and a valvuloplasty was indicated in 7 patients. Nine patients (47%) died during their hospitalization. Conclusion Hemodialysis patients are particularly exposed to IE. The most appropriate preventive method is the strict observance of asepsis when handling the vascular access first and the rapid eradication of all infectious outbreaks.

Introduction Meme si l’association entre infection a Cytomegalovirus (CMV) et rejet aigu (RA) apres transplantation renale (TR) est connue depuis plusieurs annees, les liens entre ces 2 complications demeurent toujours debattus. L’objectif de notre etude etait de rapporter notre experience en ce qui concerne l’association de ces 2 complications. Description Nous avons mene une etude longitudinale, retrospective, analytique, incluant les patients transplantes du rein entre 1986 et 2018. Methodes Notre population etait subdivisee en deux groupes : un groupe A comportant les TR compliquees d’au moins un episode de RA et un groupe B incluant celles qui n’ont jamais ete compliquees de RA. Resultats Parmi les 620 patients transplantes dans notre centre au cours de la periode d’etude, 129 (20,8 %) ont presente 149 episodes de RA (20 patients ont developpe un 2eme episode). L’infection a CMV etait plus frequente dans le groupe RA(+) (35,5 % contre 16,1 % dans le groupe RA(-), p 3 mois), p = 0,0016. Des signes histologiques de CMV ont ete notes dans 5 des 45 biopsies du greffon realisees pour suspicion de RA. L’infection a CMV etait un facteur de risque independant de survenue de RA apres etude multivariee (RR = 2,275 ; p = 0,0002). Trois episodes d’infection a CMV sont survenus precocement apres un episode de RA. Le delai etait de 20, 23 et 30 jours apres l’instauration du traitement antirejet. Conclusion L’infection a CMV est consideree comme un facteur de risque de survenue de RA apres TR. Elle doit etre prevenue pour diminuer l’incidence du RA essentiellement tardif. Par ailleurs, une infection a CMV peut compliquer un RA traite du fait de l’intensification de l’immunosuppression.

Kidney transplantation (KT) is an optimal treatment for end-stage renal disease (ESRD). Currently, short-term KT outcomes are indeed excellent, but long-term successful outcomes are still difficult to achieve, and improving them is crucial for kidney recipients. An early and accurate prediction of long-term graft survival helps healthcare practitioners to create a more personalized treatment plans for patients and facilitates the performance of clinical trials. In this study, we propose a machine learning framework to early predict graft survival after five years of KT and determine the most influential parameters that affect the survival. Our dataset was collected from Charles Nicolle Hospital in Tunis in Tunisia and it included pre, peri, post KT aspects. We utilized four machine learning algorithms to select the most important features: the least absolute shrinkage and selection operator logistic regression (Lasso-LR), Random Forrest (RF), Decision Tree (DT), and Chi-square (Chi-sq). We utilized three Scikit-learn functions to implement those algorithms: SelectFromModel (SFM), Recursive Feature Elimination (RFE), and SelectKBest (SKB). Five algorithms were utilized to builds prediction models based on the data groups resulted from the feature selection step: logistic regression (LR), k-nearest neighbors (KNN), extreme gradient boosting (XGB), and artificial neural network (ANN). We evaluated the models using five performance measures: accuracy, sensitivity, specificity, F1 measure, and area under the curve (AUC). XGBoost resulted the best model with the highest AUC (89.7%). It was based ten features selected by RF algorithm and SFM function. The accuracy, sensitivity, specificity, and F1 of the best model were 91.5%, 91.9%, 87.5%, and 89.6%, respectively. This study proposes a novel approach for investigating long-term allograft survival while considering the complex relationship between all KT aspects and long-term outcomes. Our framework can be used as a decision support system for Nephrologists to early detect graft status, which helps in developing safer recommendations for kidney patients and consequently obtaining positive KT outcomes and mitigating the risks of graft failure.

Any biomedical device requiring power from a source other than the human body or gravity is considered an active device. Currently available active biomedical devices encompass an enormous variety of technologies, ranging from large imaging machines to miniature implantable stimulators. These devices are vulnerable to cybersecurity threats, especially for devices capable of communication with an internet network. An attack exploiting these vulnerabilities can cause a variety of consequences, including data theft, denial-of-service, and serious patient harm. The chapter provides a comprehensive review of cyberattacks on biomedical devices in a hierarchical layered framework (e.g., sensing, communication, and control) with three specific attacks as case studies: (1) MRI unit-based attack, (2) infusion pump-based attack, and (3) implantable medical device attack.