Machine learning-based prediction of antibiotic resistance in Mycobacterium tuberculosis clinical isolates from Uganda.

Background: Efforts toward tuberculosis management and control are challenged by the emergence of Mycobacterium tuberculosis (MTB) resistance to existing anti-TB drugs. This study aimed to explore the potential of machine learning algorithms in predicting drug resistance of four anti-TB drugs (rifampicin, isoniazid, streptomycin, and ethambutol) in MTB using whole-genome sequence and clinical data from Uganda. We also assessed the model's generalizability on another dataset from South Africa.

The rise of pathogen genomics in Africa.

The routine genomic surveillance of pathogens in diverse geographical settings and equitable data sharing are critical to inform effective infection control and therapeutic development. The coronavirus disease 2019 (COVID-19) pandemic highlighted the importance of routine genomic surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to detect emerging variants of concern. However, the majority of high-income countries sequenced >0.

The Ugandan sickle Pan-African research consortium registry: design, development, and lessons.

Background: Sub-Saharan Africa bears the highest burden of sickle cell disease (SCD) globally with Nigeria, Democratic Republic of Congo, Tanzania, Uganda being the most affected countries. Uganda reports approximately 20,000 SCD births annually, constituting 6.67% of reported global SCD births.

Generalizability of machine learning in predicting antimicrobial resistance in E. coli: a multi-country case study in Africa.

Background: Antimicrobial resistance (AMR) remains a significant global health threat particularly impacting low- and middle-income countries (LMICs). These regions often grapple with limited healthcare resources and access to advanced diagnostic tools. Consequently, there is a pressing need for innovative approaches that can enhance AMR surveillance and management.

Virtual reality technology for surgical learning: qualitative outcomes of the first virtual reality training course for emergency and essential surgery delivered by a UK-Uganda partnership.

Introduction: The extensive resources needed to train surgeons and maintain skill levels in low-income and middle-income countries (LMICs) are limited and confined to urban settings. Surgical education of remote/rural doctors is, therefore, paramount. Virtual reality (VR) has the potential to disseminate surgical knowledge and skill development at low costs.