Giardia lamblia is one of the most common waterborne pathogens in the world and affects millions of people each year. It causes an illness known as giardiasis – with symptoms of diarrhoea, abdominal pain, and weight loss. Scientists have now developed a portable and cost-effective optical sensor for automated detection of waterborne pathogens in water samples.
Giardia lamblia is a waterborne parasite that affects millions of people every year worldwide, causing a diarrheal illness known as giardias.
Timely detection of the presence of the cysts of this parasite in drinking water is important to prevent the spread of the disease, especially in resource-limited settings.
Scientists from the University of California, Los Angeles (UCLA) have developed a field portable and cost-effective optical sensor for automated detection and counting of waterborne pathogens in water samples.
Published in Nanophotonics, their research provides extended experimental testing and evaluation of the performance and repeatability of a field-portable and cost-effective microscopy platform for automated detection and counting of Giardia cysts in water samples, including tap water, non-potable water, and pond water.
The researchers’ compact platform consists of a smartphone-based fluorescence microscope, a disposable sample cassette, and a custom developed smartphone application, called a GiardiaAnalyzer that is powered by machine learning.
Current method is time-inefficient
The standard method to detect the presence of this parasite in water samples is defined by US Environmental Protection Agency (EPA) and is based on filtration, immunomagnetic separation of Giardia cysts from other particulates in a concentrated sample, and fluorescence labelling and detection of cysts using a benchtop microscope by an expert.
Although this method provides high sensitivity and specificity, it takes 24-72 hours to analyse each sample and necessitates an expert to operate devices for sample preparation and detection and identification of cysts in water samples.
Handy, speedy automatic detection
The researchers at UCLA designed a handheld optical platform for automated detection and identification of Giardia cysts in water samples.
The platform consists of a smartphone-based fluorescence microscope, a disposable sample processing cassette, and a smartphone application that is powered by machine learning. The automated platform is extremely light with a detection accuracy of ~95% for water samples taken from different sources such as tap water and pond water.
The total time to result, from taking the sample collection from a water source, preparing the sample after which it undergoes a cyst count, is about one hour.
This platform could be especially useful in resource limited settings such as rural areas or military bases in remote locations.
Timely and accurate detection is needed
Chlorination is used for inactivation of the pathogen in water samples, however, its thick cell wall makes it moderately resistant to chlorine.
Therefore, timely and accurate detection of this parasite in water samples is important to prevent the spread of the disease.
The researchers’ study presents a novel smartphone-based optical platform for automated detection and counting of Giardia cysts in drinking water samples in one hour, including the time required for sample collection, preparation, and optical detection, with an accuracy of ~95%.
Read the original article here:
Hatice Ceylan Koydemir, Steve Feng, Kyle Liang, Rohan Nadkarni, Parul Benien, Aydogan Ozcan: Comparison of supervised machine learning algorithms for waterborne pathogen detection using mobile phone fluorescence microscopy. 14.06.2017