Sustainable integration of artificial intelligence and machine learning approaches within the African infectious disease vaccine research and development ecosystem.

Artificial Intelligence and Machine Learning (AI/ML) techniques, including reverse vaccinology and predictive models, have already been applied for developing vaccine candidates for COVID-19, HIV, and Hepatitis, streamlining the vaccine development lifecycle from discovery to deployment. The application of AI and ML technologies for improving heath interventions, including drug discovery and clinical development, are expanding across Africa, particularly in South Africa, Kenya, and Nigeria. Further initiatives are required however to expand AI/ML capabilities across the continent to ensure the development of a sustainable ecosystem including enhancing the requisite knowledge base, fostering collaboration between stakeholders, ensuring robust regulatory and ethical frameworks and investment in requisite infrastructure.

Clinical phenotype of COVID-19 vaccine-associated myocarditis in Victoria, 2021-22: a cross-sectional study.

Objectives: To describe myocarditis as an adverse event after coronavirus disease 2019 (COVID-19) vaccination, including a detailed description of clinical phenotypes and diagnostic test results and differences by age, sex, and degree of troponin level elevation.

Study Design: Retrospective cross-sectional study.

Setting, Participants: Cases of suspected myocarditis following the administration of a COVID-19 vaccine in Victoria during 22 February 2021 - 30 September 2022 reported to Surveillance of Adverse Events Following Vaccination In the Community (SAEFVIC), with symptom onset within 14 days of vaccination, and deemed to be confirmed myocarditis according to the Brighton Collaboration Criteria.

Dynamics of the blood plasma proteome during hyperacute HIV-1 infection.

The complex dynamics of protein expression in plasma during hyperacute HIV-1 infection and its relation to acute retroviral syndrome, viral control, and disease progression are largely unknown. Here, we quantify 1293 blood plasma proteins from 157 longitudinally linked plasma samples collected before, during, and after hyperacute HIV-1 infection of 54 participants from four sub-Saharan African countries. Six distinct longitudinal expression profiles are identified, of which four demonstrate a consistent decrease in protein levels following HIV-1 infection.