Africa is the second populous continent, and its population has the fastest growing rate. Some African countries are still plagued by poverty, poor sanitary conditions and limited resources, such as clean drinking water, food supply, electricity, and effective waste management systems. Underfunded healthcare systems, poor training and lack of awareness of policies and legislations on handling medical waste have led to increased improper handling of waste within hospitals, healthcare facilities and transportation and storage of medical waste. Some countries, including Ethiopia, Botswana, Nigeria and Algeria, do not have national guidelines in place to adhere to the correct disposal of such wastage. Incineration is often the favoured disposal method due to the rapid diminishment of up to 90% of waste, as well as production of heat for boilers or for energy production. This type of method – if not applying the right technologies – potentially creates hazardous risks of its own, such as harmful emissions and residuals. In this study, the sustainability aspects of medical waste management in Africa were reviewed to present resilient solutions for health and environment protection for the next generation in Africa. The findings of this research introduce policies, possible advices and solutions associated with sustainability and medical waste management that can support decision-makers in developing strategies for the sustainability by using the eco-friendly technologies for efficient medical waste treatment and disposal methods and also can serve as a link between the healthcare system, decision-makers, and stakeholders in developing health policies and programmes.

During pregnancy, a number of biomechanical and hormonal changes occur that can alter spinal curvature, balance, and gait patterns by affecting key areas of the human body. This can greatly impact quality of life (QOL) by increasing back pain and the risk of falls. These effects are likely to be the ultimate result of a number of hormonal and biomechanical changes that occur during pregnancy. Research Question and Methodology: Using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, this systematic review sets out to analyse all available literature relating to the biomechanics factors caused by pregnancy and assess how this might reduce QOL. Fifty papers were deemed eligible for inclusion in this review based on the PUBMED and SCOPUS databases. Results: Angles of lordosis and kyphosis of the spine are significantly increased by pregnancy, but not consistently across all studies. Back pain is significantly increased in pregnant women, although this is not significantly correlated with spinal changes. Increased movements of centre of pressure (COP) and increased stability indexes indicate postural control is reduced in pregnancy. Trunk range of motion, hip flexion, and extension are reduced, as well as decreased stride length, decreased gait velocity, and increased step width; again, not consistently. It is likely that each woman adopts unique techniques to minimise the effects, for example increasing step width to improve balance. Further research should focus on how altered limb kinematics during gait might affect QOL by influencing the human body, as well as assessing parameters in all planes to develop a wider understanding of pregnant biomechanical alterations.

Female sex, older age, and winter months were found to be significant determinants of recurrent human brucellosis. Enhanced surveillance systems with an emphasis on these population characteristics will allow effective preventive and protective measures to be implemented and might alleviate the recurrence of brucellosis in the country.

No abstract

Diabetic foot ulcers (DFUs) are a serious complication for people with diabetes. They result in increased morbidity and pressures on health system resources. Developments in machine learning (ML) offer an opportunity for improved care of individuals at risk of DFUs, to identify and synthesise evidence about the current uses and accuracy of ML in the interventional care and management of DFUs, and, to provide a reference for areas of future research. PubMed, Google Scholar, Web of Science and Scopus were searched using the Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies (PRISMA-DTA) guidelines for papers involving ML and DFUs. In order to be included, studies needed to mention ML, DFUs, and report relevant outcome measures regarding ML algorithm accuracy. Bias in included studies was assessed using the quality assessment tool for diagnostic accuracy (QUADAS-2). 37 out of 3769 papers were included after applying eligibility criteria. Included papers reported accuracy measures for multiple types of ML algorithms in DFU studies. Whilst varying across the ML algorithm used, all studies reported at least 90% accuracy compared to gold standards using a minimum of one reported ML algorithm for processing or recording data. Applications where ML had positive effects on DFU data analysis and outcomes include image segmentation and classification, raw data analysis and risk assessment. ML offers an effective and accurate solution to guide analysis and procurement of data from interventions which are designed for the care of DFUs in small samples and study conditions. Current research is limited, and, for the development of more applicable ML algorithms, future research should address the following: direct comparison of ML applications with current standards of care, health economic analyses and large scale data collection. There is currently no evidence to confidently suggest that ML methods in DFU diagnosis are ready for implementation and use in healthcare settings.