Latest News Archive

Please select Category, Year, and then Month to display items
Previous Archive
25 January 2024 | Story Leonie Bolleurs | Photo Sonia Small
Prof Corinna Walsh
Prof Corinna Walsh says the PEA POD Infant Body Composition System works by directly measuring an infant’s body weight and volume, and then uses these measurements to calculate the body fat percentage, fat mass, and fat-free mass.

Nutritional and growth patterns during early life have been associated with health, development, and well-being throughout the life cycle. It is also associated with risks for developing obesity and non-communicable diseases, such as cardiometabolic diseases, later in life. These are the findings of Prof Corinna Walsh, Professor in the Department of Nutrition and Dietetics.

Maternal and child health

”In line with national priorities, a strong research focus area of the Faculty of Health Sciences and the School of Health and Rehabilitation Sciences is maternal and child health,” she says. She goes on to mention that the Department of Nutrition and Dietetics has established a reputable research programme. This programme focuses primarily on the nutritional status of pregnant women and how the early environment to which they are exposed during and after pregnancy affects short- and long-term health outcomes of the offspring.

“In our previous work, the assessment of birth outcomes of infants was, however, limited by the lack of equipment to analyse body composition. The research that we can conduct with the PEA POD® provides us with immense additional potential,” remarks Prof Walsh.

She explains, “The PEA POD Infant Body Composition System is an infant-sized air displacement plethysmography system. It works by directly measuring an infant’s body weight and volume, and then uses these measurements to calculate the body fat percentage, fat mass, and fat-free mass.

According to her, the assessment of body volume takes two minutes. “The PEA POD technique also does not require collection of any fluids and does not expose the infant to radiation. It can be performed as often as required without any risks and be used up to a maximum of 8-10 kg body weight, from birth to about eight months,” she says.

Advanced technology

In the context of research on infant body weight and composition, there is a need for accurate measurement techniques that can differentiate between fat mass and fat-free mass. Prof Walsh is of the opinion that traditional measures such as body mass index (BMI) and weight for length have limitations in this regard, as they do not provide a clear distinction between these components. Furthermore, BMI may not be reliable for assessing adiposity or obesity in paediatric populations, and it can vary significantly with age and gender.

Addressing these challenges, the PEA POD equipment offers advanced technology that allows for highly accurate quantification of infant body composition. This technological capability opens up opportunities to study the effects of early-life nutrition on growth and the developmental mechanisms that may lead to later comorbidities. So, when it comes to researching infant body weight and composition, the PEA POD equipment plays a crucial role in providing precise data and insights.

News Archive

Researcher works on finding practical solutions to plant diseases for farmers
2017-10-03

 Description: Lisa read more Tags: Plant disease, Lisa Ann Rothman, Department of Plant Sciences, 3 Minute Thesis,  

Lisa Ann Rothman, researcher in the Department of
Plant Sciences.
Photo: Supplied

 


Plant disease epidemics have wreaked havoc for many centuries. Notable examples are the devastating Great Famine in Ireland and the Witches of Salem. 

Plant diseases form, due to a reaction to suitable environments, when a susceptible host and viable disease causal organism are present. If the interactions between these three factors are monitored over space and time the outcome has the ability to form a “simplification of reality”. This is more formally known as a plant disease model. Lisa Ann Rothman, a researcher in the Department of Plant Sciences at the University of the Free State (UFS) participated in the Three Minute Thesis competition in which she presented on Using mathematical models to predict plant disease. 

Forecast models provide promise fighting plant diseases
The aim of Lisa’s study is to identify weather and other driving variables that interact with critical host growth stages and pathogens to favour disease incidence and severity, for future development of risk forecasting models. Lisa used the disease, sorghum grain mold, caused by colonisation of Fusarium graminearum, and concomitant mycotoxin production to illustrate the modelling process. 

She said: “Internationally, forecasting models for many plant diseases exist and are applied commercially for important agricultural crops. The application of these models in a South African context has been limited, but provides promise for effective disease intervention technologies.

Contributing to the betterment of society
“My BSc Agric (Plant Pathology) undergraduate degree was completed in combination with Agrometeorology, agricultural weather science. I knew that I wanted to combine my love for weather science with my primary interest, Plant Pathology. 
“My research is built on the statement of Lord Kelvin: ‘To measure is to know and if you cannot measure it, you cannot improve it’. Measuring the changes in plant disease epidemics allows for these models to be developed and ultimately provide practical solutions for our farmers. Plant disease prediction models have the potential ability to reduce the risk for famers, allowing the timing of fungicide applications to be optimised, thus protecting their yields and ultimately their livelihoods. I am continuing my studies in agriculture in the hope of contributing to the betterment of society.” 

We use cookies to make interactions with our websites and services easy and meaningful. To better understand how they are used, read more about the UFS cookie policy. By continuing to use this site you are giving us your consent to do this.

Accept