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10 December 2018 | Story Leonie Bolleurs | Photo Leonie Bolleurs
One step closer to treat HIV/Aids
Nthabiseng Mokoena is working on an article based on her research about drug development in infection models, which will be published under the Research Chair in Pathogenic Yeasts.

South Africa has the biggest and most high-profile HIV epidemic in the world, with an estimated seven million people living with HIV in 2015. In the same year, there were 380 000 new infections while 180 000 South Africans died from AIDS-related illnesses. 

Invasive fungal infection, common in certain groups of patients with immune deficits, is a serious driver of global mortality in the context of the global HIV pandemic. 

“Despite a major scientific effort to find new cures and vaccines for HIV, hundreds of thousands of HIV-infected individuals continue to die on a yearly basis from secondary fungal infection. Intensive research needs to be done to help reduce the unacceptably high mortality rate due to the infection in South Africa,” said Nthabiseng Mokoena.

Mokoena is a master’s student of Prof Carlien Pohl-Albertyn, who is heading the Research Chair in Pathogenic Yeasts in the Department of Microbial, Biochemical and Food Biotechnology at the University of the Free State (UFS). 

She received her master’s degree at the December graduations of the UFS. Her thesis is titled: Caenorhabditis elegans as a model for Candida albicans-Pseudomonas aeruginosa co-infection and infection induced prostaglandin production.

Research Chair in Pathogenic Yeasts

Earlier this year, the National Research Foundation approved the Research Chair in Pathogenic Yeasts. One of the projects of the group of scientists in this chair include a study of the interaction between the yeast, Candida albicans and the bacterium, Pseudomonas aeruginosa in different hosts, using a variety of infection models.

In her research, Mokoena studied the response of infectious pathogens such as yeasts and bacteria, using a nematode (little roundworm) as an infection model to mimic the host environment. Nematodes have a number of traits similar to humans. It is thus a good alternative for humans as infection models, as it is unethical to use the latter.

Nematodes have a number of advantages, including its low cost and fast reproduction and growth. 

Mokoena monitored the survival of the nematodes to see how infectious the pathogens are, especially in combination with each other. 

Role of infection model for drug development

When these two pathogens were studied in a lab (in vitro), it was found that they can inhibit each other, but after studying them in the infection model (in vivo), Mokoena showed that these pathogens are more destructive together. 

This finding has a huge impact for the pharmaceutical industry, as it can provide information on how drugs need to be designed in order to fight infectious diseases where multiple organisms cause co-infections.

Many pathogens are resistant to drugs. Through this model, drugs can be tested in a space similar to the human body. Seeing how pathogens react to drugs within a space similar to the human body, can contribute to drug development. 

Not only are drugs developed more effectively through this model, it is also less expensive. 

It is the first time that the combination of the yeast, Candida albicans and the bacterium, Pseudomonas aeruginosa, is being experimented on in this model. 

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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