Sickle Cell Disease in Mauritania: epidemiological, clinical and therapeutic aspects about 135 cases.

OBJECTIVE: To describe the epidemiological, clinical and therapeutic characteristics of a series of sickle cell disease cases collected in Mauritania over a three-year period. METHODS: This is a descriptive study of the profile of sickle cell disease, diagnosed in Mauritania, from January 1, 2015 to December 31, 2017, at the outpatient clinics of the National Hospital Center and at the Mauritanian Association for the Support of Sickle Cell Patients. Patients were included following diagnostic confirmation by hemoglobin electrophoresis. RESULTS: During the study period, a total of 135 patients were included (79 female and 56 male), i.e. a sex ratio of 0.7 and an average age of 24 years (extremes: 9 months -77 years). All Mauritanian ethnic groups were affected by sickle cell disease, mainly the Peulths (63.7%). Sickle cell disease was found in eight wilayas, particularly Brakna (39%). The age of diagnosis was between 3 and 5 years, in 48% of patients. Sickle cell disease was discovered at the stage of complications in twelve patients. There are three types of sickle cell phenotypes: SS (54%), AS (40%) and SC (6%). In addition to transfusion, preventive treatment consisted of folic acid (n=53), hydroxyurea (n=14), and long-term antibiotic prophylaxis (n=3). CONCLUSION: The profile of sickle cell disease in Mauritania remains attributed to the lack of an active screening strategy and rapid diagnosis, hence the importance of developing a national program for early detection and management.

Deep Learning Algorithms Achieved Satisfactory Predictions When Trained on a Novel Collection of Anticoronavirus Molecules.

Drug discovery and repurposing against COVID-19 is a highly relevant topic with huge efforts dedicated to delivering novel therapeutics targeting SARS-CoV-2. In this context, computer-aided drug discovery is of interest in orienting the early high throughput screenings and in optimizing the hit identification rate. We herein propose a pipeline for Ligand-Based Drug Discovery (LBDD) against SARS-CoV-2. Through an extensive search of the literature and multiple steps of filtering, we integrated information on 2,610 molecules having a validated effect against SARS-CoV and/or SARS-CoV-2. The chemical structures of these molecules were encoded through multiple systems to be readily useful as input to conventional machine learning (ML) algorithms or deep learning (DL) architectures. We assessed the performances of seven ML algorithms and four DL algorithms in achieving molecule classification into two classes: active and inactive. The Random Forests (RF), Graph Convolutional Network (GCN), and Directed Acyclic Graph (DAG) models achieved the best performances. These models were further optimized through hyperparameter tuning and achieved ROC-AUC scores through cross-validation of 85, 83, and 79% for RF, GCN, and DAG models, respectively. An external validation step on the FDA-approved drugs collection revealed a superior potential of DL algorithms to achieve drug repurposing against SARS-CoV-2 based on the dataset herein presented. Namely, GCN and DAG achieved more than 50% of the true positive rate assessed on the confirmed hits of a PubChem bioassay.