University of Somalia

Background: In areas with poor vaccination rates, diphtheria, a dangerous acute infectious disease brought on by Corynebacterium diphtheria, remains a threat. Due to low vaccination uptake and undeveloped immunity, children under five are especially at risk. Objective: risk factors and describing the clinical characteristics of diphtheria in children under five who were enrolled in De Martino Hospital in Mogadishu were the objectives of this study. Methods: Eighty confirmed cases of diphtheria participated in a descriptive cross-sectional investigation. Structured questionnaires and a review of medical records were used to gather data. Age, gender, immunization status, clinical characteristics, complications, and results were among the variables evaluated. Findings: Of the 80 children, 43.8% were younger than two years old, and 35.0% were between three and four years old. With 50.0% of the population being female and 48.8% being male, the gender distribution was almost equal. Only 15.0% of people were fully vaccinated, 21.3% were partially vaccinated, and 63.8% were not immunized. The two most common symptoms were fever (100%) and sore throat (100%). The following conditions were also quite common: dysphagia (93.8%), respiratory distress (98.8%), cervical lymphadenopathy (96.3%), and pseudo membrane development (98.8%). 25% of patients had myocarditis, and 75% of patients experienced respiratory problems. Residence of the patients (45.0%) resided in Yaqshid, followed by 16.3% from Karaan and 8.8% from Shibis. Smaller proportions of patients came from Dharkenley (6.3%), Celasha Biyaha (5.0%), Deynile (3.8%), Hodan (3.8%), and other areas including Kaxda, Hilwa, Sh. Dhexe, Hirshabele, Balcad, and Tabelaha, each contributing 2.5% to 1.3% of cases. This distribution indicates that Yaqshiid and Karaan were the most affected areas, suggesting a potential geographic clustering of cases., 76.3% recovered from the disease, while 23.8% did not. 77.5% was survived, while 22.5% was died, children who were not vaccinated having a higher death rate. Conclusion: diphtheria is still a serious public health issue in Somalia affecting children under five. The most frequent clinical symptoms were fever, sore throat, and pseudo membrane formation, while the main risk factors were found to be incomplete immunization, malnutrition, and delayed healthcare-seeking behavior. Improved immunization campaigns, early diagnosis, and prompt treatment treatments are critically needed, as highlighted by the high case fatality rate.

Artificial intelligence (AI) is rapidly transforming higher education by providing personalized and adaptive learning experiences. However, limited research has systematically examined the mechanisms through which AI-assisted learning enhances students' self-regulated learning, particularly in developing countries. Grounded in Social Cognitive Theory, Self-Regulated Learning Theory, and the Technology Acceptance Model, this study investigates the influence of AI-Assisted Learning on Self-Regulated Learning, examining the mediating roles of AI Literacy and Critical Thinking and the moderating role of AI Self-Efficacy. A quantitative cross-sectional design was employed, and survey data were collected from 225 university students enrolled in public and private universities in Somalia. The proposed model was tested using Hayes' (2022) PROCESS Macro through regression, mediation, and moderation analyses with 5,000 bootstrap resamples. The findings indicate that AI-Assisted Learning significantly improves Self-Regulated Learning, AI Literacy, and Critical Thinking. Moreover, AI Literacy and Critical Thinking significantly enhance Self-Regulated Learning and partially mediate the relationship between AI-Assisted Learning and Self-Regulated Learning, with Critical Thinking demonstrating the stronger indirect effect. The results further reveal that AI Self-Efficacy positively moderates the relationship between AI-Assisted Learning and AI Literacy, indicating that students with greater confidence in using AI technologies gain stronger learning benefits. By integrating technological, cognitive, and psychological factors into a unified framework, this study addresses an important gap in the AI-in-education literature and provides empirical evidence from an underexplored context. The findings demonstrate that AI-assisted learning promotes autonomous learning by fostering AI literacy, critical thinking, and self-efficacy, offering valuable theoretical and practical implications for the effective integration of AI in higher education.

Acute stroke remains a major cause of death and long-term disability, with outcomes strongly dependent on rapid diagnosis and timely treatment; however, significant gaps in access and care persist. Artificial intelligence (AI) offers promising support for stroke diagnosis, treatment optimization, and workflow efficiency, although challenges related to validation, bias, regulation, and real-world impact remain. This review examines the current and emerging AI applications in acute stroke management, critically highlighting their limitations. Relevant English-language peer-reviewed and grey literature were identified through searches of PubMed, Scopus, Web of Science, and Google Scholar, with an emphasis on recent studies. No restrictions were placed on the publication year to allow the inclusion of foundational studies; however, priority was given to recent literature to ensure the relevance and currency of the evidence. An iterative thematic synthesis approach was used to identify the recurring concepts, trends, and challenges across the selected literature. AI is increasingly reshaping acute stroke care by enhancing diagnostic speed and accuracy, optimizing treatment selection, supporting clinical decision-making, and improving workflow efficiency across pre-hospital, hospital, and rehabilitation settings. Through applications in neuroimaging interpretation, large vessel occlusion detection, prognostication, and AI-powered clinical decision support systems, AI has the potential to reduce treatment delays and improve outcomes. However, challenges related to data quality, generalizability, interpretability, clinician trust, regulatory approval, and ethical concerns, particularly bias and equity, remain significant. Continued validation, transparent design, multidisciplinary collaboration, and careful integration into clinical workflows are essential to fully realize the benefits of AI in the management of stroke.

Global non-communicable diseases account for 74

Mogadishu continues to experience recurrent waterborne disease outbreaks, particularly acute watery diarrhoea (cholera), despite sustained public health interventions. Since 2017, the Banadir region has experienced uninterrupted cholera transmission. This commentary evaluates the performance of ongoing interventions in Mogadishu, identifies structural challenges, and proposes actionable strategies for sustainable control. Recent data from 2024-2025 indicate persistent cholera transmission in Mogadishu, driven by El Niño-related flooding, weak water and sanitation systems, and fragile health infrastructure. Although oral cholera vaccination (OCV), surveillance, and case management have improved, their impacts remain short-lived. Structural constraints, including inadequate WASH services, displacement-related crowding, unstable funding, and limited community behaviour change, continue to undermine progress. Evidence suggests interventions are underperforming rather than failing, due to short-term humanitarian designs and limited systemic investment. Mogadishu's experience highlights the limits of reactive cholera control in fragile urban settings. Strengthening surveillance, institutionalising area-based WASH improvements, embedding community-level behaviour change, and securing predictable multi-year financing are essential to shift from outbreak response to sustainable prevention.