Potential for high dynamic range Sedia Limiting Antigen antibody assay to support viral load monitoring during antiretroviral therapy.

Introduction: Viral Load (VL) monitoring is a crucial component of patient care during antiretroviral therapy (ART) but is not routinely available in many resource-constrained settings, where millions of patients will require care for decades to come. We hypothesise a serologic 'recent infection' test (Sedia LAg assay) which has a high dynamic range for detecting antigen-driven antibody response can provide informative proxies for VL trajectories.

Methods: A retrospective study where we analysed data linked via specimens in a well-described repository for recent infection test benchmarking (CEPHIA collaboration).

Aligning the principles and practice of research integrity and research fairness in global health: a mixed-methods study.

Introduction: In the past decade, global health research has seen a growing emphasis on research integrity and fairness. The concept of research integrity emerged in response to the reproducibility crisis in science during the late 2000s. Research fairness initiatives aim to enhance ownership and inclusivity in research involving partners with varying powers, decision-making roles and resource capacities, ultimately prioritising local health research needs.

The Biodistribution and Utility of Tc-Ethylenedicysteine-Deoxyglucose (Tc-Glucosamine) in the Identification of Active Disease in Patients with Rheumatoid Arthritis-a Single Center Prospective Study.

Purpose: Our objectives were to investigate the utility of Tc-ethylenedicysteine-deoxyglucose (ECDG) in identifying active disease in the joints of patients with rheumatoid arthritis (RA), as well as to evaluate the biodistribution of this radiopharmaceutical.

Methods: A prospective study was conducted at the Department of Nuclear Medicine of the University of the Free State/Universitas Academic Hospital in Bloemfontein, South Africa. Twenty-two participants from the rheumatology department diagnosed with RA according to the ACR/EULAR classification criteria were enrolled.

Elicitation of domain knowledge for a machine learning model for paediatric critical illness in South Africa.

Objectives: Delays in identification, resuscitation and referral have been identified as a preventable cause of avoidable severity of illness and mortality in South African children. To address this problem, a machine learning model to predict a compound outcome of death prior to discharge from hospital and/or admission to the PICU was developed. A key aspect of developing machine learning models is the integration of human knowledge in their development.

Development of artificial neural network models for paediatric critical illness in South Africa.

Objectives: Failures in identification, resuscitation and appropriate referral have been identified as significant contributors to avoidable severity of illness and mortality in South African children. In this study, artificial neural network models were developed to predict a composite outcome of death before discharge from hospital or admission to the PICU. These models were compared to logistic regression and XGBoost models developed on the same data in cross-validation.