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16 May 2025 | Story André Damons | Photo Supplied
Prof Wynand Goosen
Prof Wynand Goosen, Professor and Lead for One Health in the Department of Microbiology and Biochemistry at the University of the Free State was nominated in the TW Kambule-NSTF Award: Researcher category of the 2024/25 NSTF-South32 Awards.

Being nominated for a ‘Science Oscar’ is exciting and validates nominees’ efforts, particularly in a field as challenging and essential as infectious diseases, for which they are recognised at the highest level. 

This is according to Prof Wynand Goosen, Professor and Lead for One Health in the Department of Microbiology and Biochemistry at the University of the Free State (UFS). He was nominated in the TW Kambule-NSTF Award: Researcher category of the 2024/25 NSTF-South32 Awards for his landmark discovery of Mycobacterium bovis infection in humans in South Africa – the first confirmed cases in the country. 

Prof Goosen, who previously won the NSTF-South32 Emerging Researcher Award, says the nomination is a powerful affirmation of the impact that focused, interdisciplinary research can have. It reflects not only his personal commitment but also the dedication of a talented and hard-working team. “I am honoured and humbled to be nominated. It is also a testament to the support and vision of UFS, particularly as we position ourselves as leaders in One Health research in South Africa,” he says. 

 

Focus of research 

He was nominated by Prof Vasu Reddy, UFS Deputy Vice-Chancellor: Research and Internationalisation, and Prof Paul Oberholster, Dean for the Faculty of Natural and Agricultural Sciences (NAS) at the UFS, and Prof Nico Gey van Pittius and Prof Elmi Muller from Stellenbosch University (US). The NSTF Awards, known as the ‘Science Oscars’of SA, honour, reward, celebrate, profile and promote outstanding contributions to science, engineering and technology (SET) and innovation in South Africa.

“The nomination,” Prof Goosen continues, “recognises our work in the field of zoonotic tuberculosis (TB) and other emerging infectious diseases at the human-animal-environment interface. Our research focuses on the molecular detection and characterisation of pathogenic mycobacteria in wildlife, livestock, and human populations, with the aim of informing better surveillance, diagnostics, and control strategies — particularly in high-risk ecosystems. This includes novel applications in wildlife TB surveillance and understanding the transmission dynamics between animals and people.”

 

Establishing a Kovsie One Health Research Unit

This research is critically important as South Africa continues to face a high burden of tuberculosis, including zoonotic TB, which often goes under-detected in rural and wildlife-rich areas. Understanding how these pathogens circulate between humans, animals, and the environment, explains Prof Goosen, is essential for effective disease control and to mitigate future pandemics. This work directly supports national health priorities, informs policy, and contributes to global strategies for One Health.

Prof Goosen and the team are in the process of laying the groundwork for the establishment of a Kovsie One Health Research Unit, which will serve as a collaborative platform for research spanning human, animal, and environmental health. One of their key projects involves expanding TB and AMR surveillance in wildlife-livestock-human interfaces, using cutting-edge diagnostics and genomic tools. They are also initiating partnerships with industry and international institutions to address emerging zoonoses and environmental pathogens in a transdisciplinary manner.

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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