Summary:
UCLA researchers in the Department of Electrical and Computer Engineering have developed deep learning-based, cost-effective, and portable optical devices that facilitate label-free, high-throughput and sensitive detection of motile parasites.
Background:
Parasitic infections are a major global public health issue, but current screening methods based on manual microscopic examination often lack the volumetric throughput and sensitivity for early diagnosis. Examples of disease caused by protozoan parasites include Human African trypanosomiasis (HAT), also known as sleeping sickness, and Chagas disease, with HAT being endemic in ~30 sub-Saharan African countries and Chagas disease affecting over 6 million people in Latin America. While early detection is crucial for treating these diseases, the standard screening tests suffer from low specificity and sensitivity. Moreover, the low sensitivity requires analytical separation devices, which limit analysis in resource-limited settings. Thus, there is a need for new screening methods with high sensitivity and throughput that can reduce costs and simplify diagnosis.
Innovation:
UCLA researchers have developed a cost-effective and field-portable optical device with label-free, high-throughput and sensitive detection of motile parasites. Rather than staining with a target analyte or biomarkers, this technique utilizes the locomotion of self-propelling parasites (or other motile microorganisms) as a biomarker and contrast mechanism. Therefore, sample preparation is simple, fast (fluid sample is prepared, screened, and analyzed, all within ~20 min), and does not require any benchtop-scale sample processing device, refrigeration, centrifugation, or purification. Compared to standard benchtop optical microscopes, this innovation provides orders of magnitude increase in the screened sample volume and is significantly more compact and lightweight (1.69 kg). Therefore, this platform has the potential to be used for sensitive and timely diagnosis of neglected tropical diseases caused by motile parasites and other parasitic infections in resource-limited regions. It can also be adopted as a high-throughput research tool to study motile microorganisms in 3D, even in optically dense fluids.
Potential Applications:
• Disease/parasite detection
• Detection of bacterial pathogens
• Medical imaging
• Optical imaging
• Research tool
• Food industry
Advantages:
• Fast, simple sample preparation
• Does not require any benchtop-scale sample processing device
• Detection of parasites at low concentrations
• Significantly more compact and lightweight
• Detection of often-neglected parasites
Related Papers (from the inventors only):
Zhang, Y., Koydemir, H.C., Shimogawa, M.M., Yalcin, S., Guziak, A., Liu, T., Oguz, I., Huang, Y., Bai, B., Luo, Y. and Luo, Y., 2018. Motility-based label-free detection of parasites in bodily fluids using holographic speckle analysis and deep learning. Light: Science & Applications, 7(1), p.108.
Reference: UCLA Case No. 2019-002