MSc (Microbiology) / Department of Microbiology / Introduction: Intestinal infections caused by soil transmitted helminth and diarhoegenic Escherichia coli (E. coli) are a major threat to the health and socio-economic wellbeing of children in developing countries. Soil-transmitted helminthes (STH), Ascaris lumbricoides (A. lumbricoides), Trichuris tricuria (T. trichuris ), Hookworms and diarhogenic E coli are transmitted through the faecal-oral route and enter the body through the ingestion of eggs (STH) or E. coli pathogens following contact with contaminated hands, food, soil or the deliberate act of eating contaminated soil. Aim: This study aim to determine the prevalence of intestinal parasitic infection and diarhoegenic E. coli on the hands of school children in the Vhembe district of South Africa. Methods: The study was conducted among school children aged 5 to15 years, attending grades 0(R) to 8 at the primary and secondary school levels in the Vhembe district region of the Limpopo province. A total of 358 hand washing samples was collected from the hands of school children using hand anionic (7X 1% quadrafos, glycol ether and dioctyl sulfoccinate sodium salt) soap solution. The Microscopic McMaster slide technique was used for the identification of intestinal parasitic eggs and the Colilert Quanti-Tray®/2000 technique was used for the enumeration of E. coli. A standardised Multiplex PCR protocol was utilized to characterize the positive pathogenic E. coli strains obtained from the Colilert Quanti-Tray®/2000. A structural questionnaire was used to associate the positive results with selected socio-demographic variables. The raw data was organized and analysed by the use of SPSS version 24 software. Results: A prevalence of 2.6% intestinal parasite was found among the study population with hookworm and Enterobius vermicularis having detection rate of 0.6% and 2.0% respectively. However there were no Ascaris lumbricoides and Trichuris trichiura detected in the study population. A prevalence of 13.4% of the samples was positive for E. coli and 4.7% were identified as pathogenic E. coli strains: Enteroaggregative Escherichia coli (EAEC), Atypical Enteropathogenic Escherichia coli (APEC), Typical Enteropathogenic Escherichia Coli (TPEC) and Enterotoxigenic Escherichia coli (ETEC) distributed with prevalence percentage of 2%, 0.3%, 1.1% and 0.3% respectively. The study also revealed a significant association between hand child hygiene with the prevalence of E. coli. Conclusion: Environmental sanitation conditions like type of toilets and lack of safe drinking water is closely associated with the prevalence of E. coli among the school going children. / NRF

The accurate identification of parasites allows for the quick diagnosis and treatment of infections. Current state-of-the-art identification techniques require a trained technician to examine prepared specimens by microscope or other molecular methods. In an effort to automate the process and better facilitate the field identification of parasites, approaches are developed to utilize LabVIEW and MATLAB, which are commercially available image processing software packages, for parasite egg identification. The goal of this project is to investigate different image processing techniques and descriptors for the detection and characterization of the following parasite eggs: Ascaris lumbricoides, Taenia sp., and Paragonimus westermani. One manual approach and four automated approaches are used to locate the parasite eggs and gather parasite characterization data. The manual approach uses manual measurements of the parasite eggs within the digital images. The four automated approaches are LabVIEW Vision Assistant scripts, MATLAB separation code, MATLAB cross-section grayscale analysis, and MATLAB edge signature analysis. Forty-four separate measurements were analyzed through the four different approaches. Two types of statistical tests, single factor global Analysis of Variance (ANOVA) test and Multiple Comparison tests, are used to demonstrate that parasite eggs can be differentiated. Thirty-six of the measurements proved to be statistically significant in the differentiation of at least two of the parasite egg types. Of the thirty-six measurements, seven proved to be statistically significant in the differentiation of all three parasite egg types. These results have shown that it is feasible to develop an automated parasite egg detection and identification algorithm through image processing. The automated image processing techniques have proven successful at differentiating parasite eggs from background material. This initial research will be the foundation for future software structure, image processing techniques, and measurements that should be used for automated parasite egg detection. / Master of Science