More than half a billion people have to survive with unimproved water, as providing safe drinking water is still a problem in many parts of the developing world. Of the waterborne pathogens, Giardia lambia (G. lambia) is one of the most common intestinal parasites that are difficult to remove using traditional water purification methods. Current methods for their detection take up to two days and require analysis laboratories with trained specialists and expensive equipment. Because of this there is an ongoing effort to design low-cost and field-portable methods that can rapidly analyse large volumes of water.
Ozcan and co-workers at UCLA have developed a method for the detection of G. lambia cysts in water using a light weight attachment to a smartphone. The attachment consists of a fluorescence microscope, aligned to the smartphone camera, and a disposable water sample cassette that can hold 20 mL of water. The whole test can be carried out in just 1 hour, from taking the sample from the source, to receiving the total number of cysts detected in the sample.
The process is relatively simple, with the test sample first being fluorescently labelled and then filtered through a membrane that traps the G. lambia cysts. A fluorescence image is taken and wirelessly transmitted to servers using an app designed by the group. Digital analysis is carried out using a machine learning algorithm that can specifically recognise the cysts over other fluorescent micro-objects. The results of this analysis are then transmitted back to the phone and displayed on the app.
The group were able to achieve an impressive limit of detection of 12 cysts per 10 mL of sample, citing several factors that led to this limit. They have put forward a number of suggestions for how they hope to further improve their system, so it will be interesting to hear more from this group.
To download the full article for free* click the link below:
Rapid imaging, detection and quantification of Giardia lamblia cysts using mobile-phone based fluorescent microscopy and machine learning
Hatice Ceylan Koydemir, Zoltan Gorocs, Derek Tseng, Bingen Cortazar, Steve Feng, Raymond Yan Lok Chan, Jordi Burbano, Euan McLeod and Aydogan Ozcan
*Access is free until 31st March 2015 through a publishing personal account. It’s quick, easy and free to register.
About the web writer
Claire Weston is currently studying for a PhD at Imperial College London, focussing on developing novel photoswitches and photoswitchable inhibitors.