Research Best Practices for Eye Stroke Vision Rehabilitation: A Qualitative Study.

Purpose: Central retinal artery occlusion, also known as an eye stroke, results in visual impairment and functional challenges. Our study objectives were to identify meaningful measures and factors that indicate or enable successful recovery after eye stroke and to determine optimal processes to support research, including exploring barriers and facilitators to successful research participation.

Methods: We used qualitative methods including the 5Ts Framework (target population identification, team composition, time considerations, tips to accommodate older adults, tools for inclusive enrollment of older adults) to provide a guide to the development of the semi-structured interviews and to help facilitate the research process such as the set-up of interviews.

Platinum nanoparticles in cancer therapy: chemotherapeutic enhancement and ROS generation.

Platinum nanoparticles (PtNPs) offer significant promise in cancer therapy by enhancing the therapeutic effects of platinum-based chemotherapies like cisplatin. These nanoparticles improve tumor targeting, reduce off-target effects, and help overcome drug resistance. PtNPs exert their anti-cancer effects primarily through the generation of reactive oxygen species (ROS), which induce oxidative stress and apoptosis in cancer cells.

Factors associated with vaccine default in Southern Ghana based on data from the RTSS malaria vaccine trial in Cape Coast.

Malaria has been a severe global and public health concern for the last couple of decades. Ghana, like many other countries in sub-Saharan Africa, is most affected by the disease, with children facing dire consequences. The recent introduction of the RTS, S malaria vaccine holds great potential in reducing malaria fatalities in children.

Predictive modelling of metabolic syndrome in Ghanaian diabetic patients: an ensemble machine learning approach.

Objectives: The burgeoning prevalence of cardiometabolic disorders, including type 2 diabetes mellitus (T2DM) and metabolic syndrome (MetS) within Africa is concerning. Machine learning (ML) techniques offer a unique opportunity to leverage data-driven insights and construct predictive models for MetS risk, thereby enhancing the implementation of personalised prevention strategies. In this work, we employed ML techniques to develop predictive models for pre-MetS and MetS among diabetic patients.