Human African trypanosomiasis or sleeping sickness is a deadly disease endemic in sub-Saharan Africa, caused by single-celled protozoan parasites. Although it has been targeted for elimination by 2020, this will only be realized if diagnosis can be improved to enable identification and treatment of afflicted patients. Existing techniques of detection are restricted by their limited field-applicability, sensitivity and capacity for automation. Microfluidic-based technologies offer the potential for highly sensitive automated devices that could achieve detection at the lowest levels of parasitemia and consequently help in the elimination programme. In this work we implement an electrokinetic technique for the separation of trypanosomes from both mouse and human blood. This technique utilises differences in polarisability between the blood cells and trypanosomes to achieve separation through opposed bi-directional movement (cell counterflow). We combine this enrichment technique with an automated image analysis detection algorithm, negating the need for a human operator.

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