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21 April 2023 | Story Andre Damons | Photo Charl Devenish
Dr Emms
Dr Ayodeji Emmanuel Ogunbayo (right), who graduated this week with a Doctor of Philosophy degree with specialisation in Medical Virology, with his proud supervisor Prof Martin Nyaga, Associate Professor in the UFS Next Generation Sequencing (UFS-NGS).

With respiratory diseases contributing to the highest morbidity and mortality rate in children, and the vast majority of disease aetiology remaining undiagnosed in clinical settings, Dr Ayodeji Emmanuel Ogunbayo hopes his research for his PhD in Medical Virology will help to reduce mortality in children with respiratory infection.

Dr Ogunbayo, commonly known as Dr Emms by his peers, graduated on Thursday (20 April 2023) with a Doctor of Philosophy degree with specialisation in Medical Virology at the University of the Free State (UFS) Faculty of Health Sciences graduation ceremony. The title of his thesis was Metagenomics of the respiratory RNA virome of children in the Free State. His supervisor was Prof Martin Nyaga, Associate Professor in the UFS Next Generation Sequencing (UFS-NGS) and Director of the WHO Collaborating Centre (WHO CC).

“While clinical metagenomics next-generation sequencing (mNGS), which is an upcoming method, has the potential to revolutionise infection diagnosis and management in children, there is a dearth of information on its clinical applicability in Africa. This is the knowledge that guided the inception of this research,” says Dr Ogunbayo.

Project came at the right time

He commenced with his PhD in Medical Virology in 2019, which was centred on the applicability of mNGS in severe acute respiratory infection (SARI) in children and deciphering the children’s respiratory virome in health and disease, a project which was the first of its kind in Africa.

According to him, the project was conceived before the COVID-19 pandemic, however, despite the challenges encountered due to lockdown measures, the project came at the right time as it was able to address several profound effects of the pandemic such as COVID-19 on the dynamics of transmission of respiratory viruses and ultimately its effect on children’s health. During his PhD study, he published four manuscripts in highly reputable journals including the most recent one which was accepted in Journal of Medical Virology (JMV)  an impressive impact factor of 20.693.

The findings from this study included a validated and robust workflow to recover respiratory RNA viruses from clinical samples, according to the graduation programme. The established workflow was adapted to decipher children’s respiratory virome composition in health and disease, with a degree of heterogeneity, while simultaneously establishing the clinical diagnostic applicability of mNGS and, more importantly, the increased utility of dual-triple mNGS analysis tools in robust detection of viral pathogens in SARI.

His hope for the research

Dr Ogunbayo says: “This research generated vast pioneering information and data in Africa that could guide and influence policy in the adoption of clinical metagenomics, especially in cases where conventional methods of diagnosis yielded no results. Hopefully, this could lessen mortality due to respiratory infection in children.

Before studying in the field of medical Microbiology and Virology, he wanted to be a psychiatric nurse, but instead chose his current field of study because of his interest in the microbial world and pathology.

“It’s a feeling one cannot really put into words. A part of me feels like it is a ‘dream come true’ and another part of me feels like ‘this is a milestone achieved, but it’s just a stepping stone to further milestones to be achieved’.

“What is next is for me to dive into the world of policy-influencing research, give back to the academic community through student supervision, and work on my journey to becoming an emerging researcher. This is the reason I have taken a postdoctoral position at the UFS-NGS Unit under the mentorship of Prof Martin Nyaga.”

Prof Nyaga says: “Looking back at Emmanuel’s doctoral journey, it brought out the best in him in every aspect of the study. Longitudinal studies are hectic in the sampling phase, and all the ethical clearances that a study must achieve prior to beginning the sampling and the laboratory work can be very frustrating. Soon after his study was approved, the pandemic lockdown level 5 rules were applied, which meant he had to pause on the study. However, his focus, positive mindset and his ability to work in close consultation with his supervisor enabled him to finish this study in the best way possible. I am very proud of him.”

News Archive

Mathematical methods used to detect and classify breast cancer masses
2016-08-10

Description: Breast lesions Tags: Breast lesions

Examples of Acho’s breast mass
segmentation identification

Breast cancer is the leading cause of female mortality in developing countries. According to the World Health Organization (WHO), the low survival rates in developing countries are mainly due to the lack of early detection and adequate diagnosis programs.

Seeing the picture more clearly

Susan Acho from the University of the Free State’s Department of Medical Physics, breast cancer research focuses on using mathematical methods to delineate and classify breast masses. Advancements in medical research have led to remarkable progress in breast cancer detection, however, according to Acho, the methods of diagnosis currently available commercially, lack a detailed finesse in accurately identifying the boundaries of breast mass lesions.

Inspiration drawn from pioneer

Drawing inspiration from the Mammography Computer Aided Diagnosis Development and Implementation (CAADI) project, which was the brainchild Prof William Rae, Head of the department of Medical Physics, Acho’s MMedSc thesis titled ‘Segmentation and Quantitative Characterisation of Breast Masses Imaged using Digital Mammography’ investigates classical segmentation algorithms, texture features and classification of breast masses in mammography. It is a rare research topic in South Africa.

 Characterisation of breast masses, involves delineating and analysing the breast mass region on a mammogram in order to determine its shape, margin and texture composition. Computer-aided diagnosis (CAD) program detects the outline of the mass lesion, and uses this information together with its texture features to determine the clinical traits of the mass. CAD programs mark suspicious areas for second look or areas on a mammogram that the radiologist might have overlooked. It can act as an independent double reader of a mammogram in institutions where there is a shortage of trained mammogram readers. 

Light at the end of the tunnel

Breast cancer is one of the most common malignancies among females in South Africa. “The challenge is being able to apply these mathematical methods in the medical field to help find solutions to specific medical problems, and that’s what I hope my research will do,” she says.

By using mathematics, physics and digital imaging to understand breast masses on mammograms, her research bridges the gap between these fields to provide algorithms which are applicable in medical image interpretation.

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