Université de Thiès

Glioblastoma (GBM) is a highly aggressive and radioresistant brain tumor with limited therapeutic options. While conventional photon radiotherapy remains standard of care, high linear energy transfer (LET) carbon ion (CI) irradiation offers enhanced biological effectiveness through the induction of complex DNA damage. The DNA damage response (DDR), particularly via ATM and ATR kinases, plays a central role in mediating cellular responses to irradiation and represents a promising target for radiosensitization. However, the systems-level molecular adaptations underlying different radiation qualities and DDR inhibition remain poorly understood. To address this, we performed an integrated multi-omics analysis combining metabolomics and phosphoproteomics in GBM cells exposed to X-rays or CI irradiation, with or without ATM inhibitor (ATMi) / ATR inhibitor (ATRi). Metabolic and phosphoproteomic data were analyzed using multivariate statistics, pathway enrichment, kinase inference, and integrative network approaches to identify coordinated molecular responses. Our results reveal marked radiation quality-dependent differences. X-ray irradiation induced irradiation-driven metabolic suppression, particularly affecting central carbon metabolism, nucleotide biosynthesis, and mitochondrial function. In contrast, CI irradiation exhibited stronger interaction effects with DDR inhibition, leading to distinct metabolic adaptations involving redox balance, lipid remodeling, and enhanced protein turnover. Phosphoproteomic analyses showed extensive signaling reprogramming in both conditions, with ATR inhibition emerging as a dominant driver of phosphorylation changes under X-rays, while CI irradiation itself was the primary determinant under high-LET conditions. Distinct kinase networks and biological processes were identified, including RNA metabolism (ATRi + X-ray), RNA transport and stress granule dynamics (ATRi + CI), cytoskeletal regulation (ATMi + X-ray), and translational control (ATMi + CI). This integrative multi-omics analysis demonstrated coordinated phosphometabolic networks, highlighting tight coupling between signaling pathways and metabolic rewiring. Network analysis identified key hubs linking amino acid metabolism, lipid dynamics, and phosphorylation-dependent signaling, with clear divergence between radiation modalities.

Background Malaria remains a major public health problem in Guinea-Bissau, where transmission is strongly influenced by climatic and environmental conditions. Despite progress, its incidence exhibits significant seasonal and geographic variations. This study aimed to analyze the influence of meteorological factors on malaria incidence, evaluate the ability of machine learning models for trend prediction, and conduct a comparative evaluation against conventional time-series approaches across the health regions of Guinea-Bissau. Methods The study used 924 monthly observations collected across Guinea-Bissau’s 11 health regions between January 2016 and December 2022, considering 28 candidate variables spanning geographical, demographic, epidemiological, environmental, and climatic dimensions. Three machine learning models Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM) were developed and benchmarked against conventional time-series models, namely SARIMA, ETS, and GLMM with Splines. Performance was evaluated using RMSE, MAE, and R², validated across temporal horizons up to 2022, and supported by residual diagnostics and spatial autocorrelation analyses (Moran's I). SHAP values were used to interpret variable importance. Results The XGBoost model achieved the best predictive performance, with an R² of 0.78 (95% CI: 0.52–0.97), alongside an RMSE of 2.27 (95% CI: 1.28–3.15) and an MAE of 1.74 (95% CI: 0.89–2.63) on the test set (2021), as well as strong validation metrics an R² of 0.91(95% CI: 0.82–0.98), an RMSE of 1.57 (95% CI: 0.87–2.24) and an MAE of 1.22 (95% CI: 0.65–1.85) on the 2022 horizon. Machine learning algorithms consistently outperformed conventional temporal models (SARIMA, ETS, and GLMM with Splines) in capturing non-linear relationships. The models maintained robust generalization without significant residual spatial autocorrelation. Conclusions Machine learning models, particularly XGBoost, significantly outperform conventional time-series approaches in predicting malaria dynamics in Guinea-Bissau. Integrating epidemiological, climatic, and environmental data improves the identification of high-risk periods, contributing to early warning systems and optimized control interventions.

Résumé : Depuis les années 1970, la recherche sur la migration internationale de retraite documente le déplacement de retraités nord-européens vers des destinations plus clémentes et plus abordables, mais elle reste concentrée sur l’Europe du Sud et le Maghreb, et s’attache surtout aux motivations sans toujours reconstituer la séquence par laquelle un touriste devient résident. Cet article examine ce processus à Saly, sur la Petite Côte sénégalaise, une station balnéaire planifiée devenue, en deux décennies, une destination d’installation pour un nombre croissant de retraités européens. À partir de seize entretiens semi-directifs menés auprès de résidents européens âgés de 60 à 75 ans, extraits d’un corpus doctoral plus large consacré à la résidentialisation du site, l’article reconstitue la séquence de pratiques par laquelle le séjour touristique se transforme en installation résidentielle, et propose une typologie à deux dimensions, durée de séjour et engagement économique dans la destination, qui combine et confronte à un même échantillon les catégories déjà proposées pour ce terrain par Quashie (2016) et Sall (2009). On peut identifier quatre grandes figures : ceux qui possèdent une résidence secondaire, ceux qui s’y installent de façon quasi-permanente, les amateurs de week-ends prolongés et les entrepreneurs venus d’Europe. Ces catégories ne sont jamais étanches car beaucoup de gens circulent entre elles au fil du temps. Certes, les raisons économiques et climatiques jouent un rôle majeur, mais elles s’inscrivent dans une dynamique Nord-Sud particulière, façonnée par des différences de pouvoir d’achat et par une histoire coloniale partagée entre la France et le Sénégal. C’est précisément ce qui rend ce contexte distinct des réalités sud-européennes ou marocaines auxquelles se réfèrent habituellement les typologies migratoires disponible dans la littérature. L’article contribue ainsi à étendre ces typologies à un contexte ouest-africain encore peu documenté, et à en préciser l’ancrage territorial et temporel. Mots-clés : Tourisme résidentiel, Migration de retraite, Typologie, Trajectoire, Saly.

Resumen: El examen de textos contemporáneos africanos traducidos aborda la cuestión de términos relacionados unos a otros al tiempo que determinan la noción de identidad en traducción. Transferir un contenido literario del francés al español puede ocasionar varios resultados conducentes cada uno a una determinada identidad del traductor. Cada elección tiene la particularidad de presentar una imagen diferente del autor del texto traducido. A veces libre de sus decisiones y en otras ocasiones despojado de esta libertad, el traductor puede navegar entre una visibilidad cultural del texto fuente o permanecer completamente oculto en el texto meta. Pero en cualquiera de sus manifestaciones, presenta una determinada identidad que depende de la ideología y de los factores implicados en las decisiones adoptadas en cada momento. Palabras clave: Identidad del traductor; traducción literaria; domesticación; extranjerización; ideología del traductor.

Introduction: Depression is the most common psychiatric complication after stroke, with a worldwide prevalence estimated at 18–60%. It nevertheless remains under-recognised and under-diagnosed in our setting, where no epidemiological data are available. Methods: We conducted a 6-month prospective study (1 November 2025 – 1 May 2026) of patients hospitalised for stroke in the Neurology Department of the National General Reference Hospital (HGRN), N’Djamena. Depression was assessed with the MADRS (threshold > 20). Results: Of 98 patients, 33 had post-stroke depression (PSD), a prevalence of 33.67%. The mean age of depressed patients was 58.51 ± 13.71 years (range: 30–84 years) and 57.58% were male. The prevalence of depression was 38.71% among married patients. At 3 months, 6 depressed patients (18%) had a favourable outcome under treatment. Conclusion: Post-stroke depression is common and affects both functional and vital prognosis. Further studies are needed to better understand its determinants and to open the way to new therapeutic interventions.