University of Kinshasa

Background: The DRC faces the paradox of high mortality and low incidence of breast cancer due to late consultations and delayed treatment; however, it lacks studies detailing the different delays of breast cancer management. We initiated this study to assess these delays and identify the associated factors. Methods: This was a cross-sectional study conducted on 201 records of women followed for breast cancer at the University Clinics of Kinshasa (UCK) from January 2020 to December 2024. Population characteristics and delays were described using descriptive statistics, logistic regression was used to identify factors associated with delays. Results: The mean age was 48±12 years. 90.8% of patients were diagnosed at advanced stages, 67% received chemotherapy as first treatment. The median patient delay was 365 days, the median diagnostic delay was 26 days, the median treatment delay was 39 days, the median health system delay was 67 days, the median total delay was 398 days. Single women had a higher risk of consultation delay (aOR=2.48; p=0.02), diagnostic delay (aOR=1.79 p=0.01) and treatment delay (aOR=3.67; p=0.04). Women who followed alternative pathways had a higher risk of delay in consultation (aOR=9; p=0.01), diagnosis (aOR=3.27; p=0.02), and treatment (aOR=3.27; p=0.01). Consulting for symptoms other than breast lump was associated with a higher risk of treatment delay (aOR=3.44; p=0.02). Conclusions: Efforts must be made to address the causes of delayed consultations and to improve the patient care pathway.

Managing continuous inventory represents a major challenge for commercial enterprises, as it requires ensuring product availability while controlling the costs associated with stockouts and overstocking. Traditional approaches, such as the Wilson model (EOQ), allow the determination of the economic order quantity; however, they remain limited when dealing with dynamic demand fluctuations. This study proposes a hybrid approach that combines the principles of mathematical inventory management with supervised machine learning models, including Random Forest, Support Vector Machine, and Gradient Boosting, integrated within a stacking framework to improve demand forecasting.The experiments are conducted using a dataset extracted from an inventory management system of a large commercial enterprise available on the Kaggle platform. The dataset includes information related to products, sales transactions, orders, dates, and replenishment statuses. The performance of the models is evaluated using statistical metrics such as the coefficient of determination (R²), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE), enabling an objective assessment of forecasting accuracy. The results demonstrate that the combination of classifiers significantly improves prediction accuracy compared with individual models. The developed models are integrated into a responsive web-based business intelligence application that provides inventory managers with real-time visualization of key indicators, including products at risk of stockouts, optimal order quantities, and variations in average stock levels. This decision-support interface facilitates inventory monitoring and enables more proactive supply planning. Overall, the results indicate that the proposed approach improves the reliability of demand forecasts and supports more efficient replenishment decisions while reducing operational costs associated with stock shortages and excess inventory.

Cette recension analyse comment le soutien au logement, couplé aux connaissances sur les compétences et qualifications du personnel transmis aux futurs migrants étudiants de l'espace grand Kasaï par leurs réseaux migratoires vivant à Kinshasa influencent leurs décisions d'émigrer. En d'autres termes, elle soutient que, l’assistance au logement (hébergement) et les informations sur les compétences et qualifications communiquées par les réseaux migratoires, comme éléments déclencheurs de l'émigration.

Bovine mastitis is an inflammatory disease of the mammary gland, largely caused by microbial infections and commonly managed through intramammary administration of antibiotics. However, the extensive and recurrent use of antibiotics has led to the emergence and spread of antibiotic-resistant pathogens, particularly multidrug-resistant (MDR) Staphylococcus aureus, posing significant veterinary and public health challenges. Herein, we investigated the potential of Commiphora swynnertonii resin-loaded nanoparticles as an antibiotic alternative. This study aimed to identify an effective nanocarrier platform for antimicrobial delivery of C. swynnertonii resin. To achieve this, different types of nanocarriers were explored: liposomes, alginate-based nanoparticles, chitosan-based nanoparticles (ChN), solid lipid nanoparticles (SLN), and nanostructured lipid carriers (NLC), each with or without hyaluronic acid-stearylamine conjugate (HAC). Antimicrobial activity was assessed against 13 MDR S. aureus strains isolated from mastitis cow patients. Based on minimum inhibitory concentrations (MIC) and minimum bactericidal concentrations (MBC) assays, the tested nanocarriers were ranked in ascending order of antimicrobial coverage as follows: liposomes (0% inhibitory and 0% bactericidal), ChN (15.4% inhibitory and 0% bactericidal), NLC (23% inhibitory and 7.6% bactericidal), SLN (69% inhibitory and 46.2% bactericidal), and alginate nanoparticles (100% inhibitory and 53% bactericidal). HAC-containing alginate nanoparticles achieved the strongest activity, with MIC 26-417µg/ mL and MBC 35-417µg/mL, followed by HAC-containing SLN with MIC 17-417µg/mL and MBC 278-417µg/mL. These findings highlight the potential of alginate-HAC nanoparticles as a promising platform for delivering C. swynnertonii resin constituents, offering a novel strategy to combat MDR mastitis pathogens through plant-based nanotherapeutics.

L’accès à une eau de boisson salubre demeure un défi majeur de santé publique dans de nombreux pays africains, notamment en République Démocratique du Congo. A Mbujimayi, la faiblesse du réseau public de distribution, la dépendance aux sources alternatives et les déficiences d’assainissement exposent les populations à des risques sanitaires importants. Cette étude a été conduite dans le but de déterminer la qualité physico-chimique et microbiologique de différentes eaux de boisson issues de différentes sources selon les saisons et d’en estimer les risques sanitaires associés, et ce, par rapport aux normes de l’Organisation Mondiale de la Santé (OMS). C’est une étude descriptive analytique transversale réalisée sur 62 échantillons d’eau collectés dans 31 sites représentant sept types de sources d’approvisionnement. Les analyses ont été effectuées selon des méthodes normalisées et des procédures opérationnelles validées. Les risques sanitaires ont été évalués par une matrice OMS et une approche QMRA. Les résultats montrent une forte variabilité saisonnière. La saison sèche est marquée par une minéralisation accrue et des dépassements de certaines valeurs guides chimiques, tandis que la saison des pluies est caractérisée par une contamination microbiologique élevée. La convergence des analyses physico-chimiques, microbiologiques et de l’évaluation quantitative du risque microbiologique (QMRA) indique que plus de 70 % des sources étudiées présentent un niveau de risque infectieux modéré à critique, incompatible avec une consommation directe sans traitement préalable. La qualité des eaux de boisson à Mbujimayi est donc globalement non conforme aux normes OMS, justifiant la mise en oeuvre urgente de stratégies intégrées de gestion du risque.

The Sichuan–Xizang Railway corridor is not only a strategic transport passage but also a distinctive and widely visited tourism route. However, empirical evidence on the interactive relationships among transport, tourism, and economy (TTE) in this corridor remains scarce, even though coordinated development of these three systems is essential for achieving high-quality growth. This study develops a ternary coupling evaluation framework and applies the Entropy Weight Method, the Coupling Coordination Degree Model, and the Relative Development Degree Model to quantify the spatiotemporal evolution of six node cities (Chengdu, Ya’an, Garze, Qamdo, Nyingchi, and Lhasa) from 2012 to 2022. The results indicate differences in temporal dynamics across subsystems. The economy grows steadily, tourism rises with pronounced fluctuations, and transport shows the strongest vulnerability to the COVID-19 shock. Spatially, CCD exhibits a persistent “dumbbell-shaped” pattern, with higher coordination at the two ends (Chengdu and Lhasa) and weaker coordination in the central section. Structurally, RDD and heatmap results indicate convergence toward a transport-lagging structure (i.e., a relative lag in carrying capacity), and ternary trajectories drift away from the transport vertex, revealing structural divergence driven by an asymmetric growth rate mismatch: tourism demand expands faster than transport supply capacity. These findings provide a pre-completion baseline for the corridor and highlight priorities for correcting subsystem imbalance, including strengthening external links in the central section, improving hub-to-scenic internal connectivity, and leveraging digital outreach to support demand monitoring and destination management.

Unconventional shale gas is vital for clean energy and energy security. However, its ultralow permeability leads to low primary recovery. This study investigates CO2-enhanced shale gas recovery (ESGR) in the Yanchang shale reservoir by using a CMG-GEM compositional simulator with a dual porosity/permeability model, evaluating well placements and both continuous and huff-and-puff injection methods. Findings show that continuous CO2 injection increases CH4 recovery by 7.59% over no injection. For huff-and-puff, a shorter 1 year injection period yielded the highest CH4 recovery; extending injection to 2 and 3 years caused declines of 1.93 and 3.89%, respectively. Conversely, using five injection cycles resulted in the highest cumulative CH4 recovery. Starting CO2 injection later in the production lifecycle also optimized recovery, with a 1.15% increase observed after 10 years of initial CH4 production, as it utilizes more favorable reservoir pressure conditions. Moreover, for CO2 storage, the reservoir exhibited 99.565% efficiency during continuous injection. In huff-and-puff, longer injection durations improved storage, with a 3 year period achieving 98.35% efficiency. Similarly, five injection cycles yielded the highest storage efficiency, at 99.12%. Delaying the injection start time also significantly enhanced CO2 storage, with efficiency improving from 96.38% after 1 year to 98.35% after 10 years, leveraging improved pressure dynamics over time. In addition, a sensitivity analysis confirmed that key reservoir parameters, such as matrix porosity, permeability, and pressure, significantly influence both gas recovery and storage capacity. Critical hydraulic fracture parameters, including half-length, spacing, conductivity, and bottom-hole pressure, are essential for optimizing gas flow and CO2 injection efficiency. This study applies to tight shale gas formations worldwide, offering insights into optimizing hydraulic design and injection strategies to enhance shale gas production and CO2 sequestration, supporting global carbon management and climate change mitigation.

The morbidity and mortality related to wound infections and microbial resistance are real public health concern in low-income settings where data are lacking, empirical antibiotic use is common and microbiological diagnostics are limited. This study aimed to describe the prevalence and antimicrobial resistance (AMR) patterns resulting from wound infections. We conducted a one-year multicenter cross-sectional study across hospitals in Goma, Democratic Republic of the Congo. Consecutive patients with clinically infected wounds were enrolled. Wound swabs were processed using standard culture and susceptibility testing. Firth’s bias-reduced logistic regression was used to assess factors associated with surgical site infection (SSI) and AMR, with analyses stratified by Gram staining. Most patients were below 40 years old, with a median age of 27 years (IQR: 22–39), predominantly males (64.5%). Gunshot injuries (33.2%), road traffic accidents (24.5%) and cesarean section (14.8%) were the leading cause of wounds. SSIs accounted for 21.25% of all clinically diagnosed infected wounds; they were significantly associated with female sex (OR = 3.65, 95% CI: 1.18–11.92, p = 0.03) and abdominal surgery (OR = 272.92, 95% CI: 85.66–1208.58, p < 0.001). In female patients, a high rate of SSIs was observed following cesarean section (86.8%). Overall microbial swab cultures were negative in 21.0%. Among 18 isolated species, Gram-negative bacteria were predominant with Proteus mirabilis (24.1%), Pseudomonas aeruginosa (13.6%), and Escherichia coli (12.7%), as the leading pathogens. In Gram-stratified Firth models, empirical antibiotic therapy was not independently associated with increased in vitro AMR. Amikacin showed a protective association among Gram-negative isolates, while estimates for Gram-positive cocci were imprecise due to small sample size. Pseudomonas aeruginosa exhibited reduced susceptibility to multiple drug classes. Wound infections in Goma are largely linked to trauma and cesarean deliveries, with Gram-negative microorganisms dominating. The microbial ecology comprises ESKAPE pathogens and species known for their emerging resistance patterns such as Proteus mirabilis, Pseudomonas aeruginosa and Escherichia coli. Empirical antibiotic therapy was not independently associated with AMR after adjustment. These findings underscore the urgent need for enhanced diagnostic strategies, strengthened infection prevention measures, and effective antimicrobial stewardship in this resource-limited and conflict-affected setting.