Project Lead(s): Udit Parekh
Issue
Poor water quality leads to more than 4 billion cases of diarrhea and more than 1.5 million deaths of children below the age of five from diarrheal disease annually, making it the second highest cause of infant mortality.
Poor water treatment and delivery infrastructure in India makes the risk of water-borne diseases extremely high.
Solution
The team proposed to develop a novel, self-contained, field-usable, rapid microbial water quality test.
While current tests can take 24 hours or more, this test would yield results within a few hours.
The project team was able to design and fabricate a field-usable, self-contained device for sample preparation (i.e., isolation and concentration of bacteria from water) for two different volumes (20 mL and 100 mL).
They also developed a novel filter treatment protocol that significantly reduces the background fluorescence, and developed an assay for the rapid detection of total and fecal coliform bacteria in water samples.
The team achieved a five-hour turnaround for the assay, as compared to more than 24 hours for culture-based tests, using fluorescent enzyme substrates. This is 10 hours faster than macro-scale fluorescent or colorimetric tests available today.
Outcome
Two versions of the novel microbial isolation and concentration device have been designed and prototyped, suitable for field use in testing water quality.
An Indian patent application and Patent Cooperation Treaty (PCT) application have also been filed for this device.
The team has designed reagents for specifically fluorescently labeling coliform and E. coli cells, using a blocking agent to prevent binding of the fluorescent substrates and labels to non-specific sites outside cells. A compact, battery-operated fluorescence imaging system for detection of these fluorescently labeled cells has also been developed.
An algorithm has been developed to differentiate fluorescent micro-colonies from other fluorescent material, using colour and shape descriptors, combined with statistical learning-based texture classification.
The aim is to complete the design and development into a complete product, ready to go to market. This process will involve optimizing the design of the device and the imaging system as well as ensuring the reagents can be manufactured to meet cost targets.
The optimized system will be validated in external settings, especially in field conditions.
The project team plans on scaling up the project by applying for Grand Challenges Canada scale-up funding.