A team of researchers at the University of California Los Angeles (UCLA) has developed an automated diagnostic test reader for antimicrobial resistance that attaches to a smartphone. The technology could lead to routine testing for antimicrobial susceptibility—which is becoming more common in bacterial pathogens responsible for high-mortality diseases such as pneumonia, diarrhea, and sepsis—in resource-poor areas.
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The smartphone attachment has a plate that can hold up to 96 wells for testing. An array of LEDs illuminates the sample, and then the phone's camera is used to sense small changes in light transmission of each well containing a different dose selected from a panel of antibiotics. Images are sent to a server to automatically perform antimicrobial susceptibility testing and the results are returned to the smartphone in about 1 min., compared to 24 hours using conventional methods.
|Schematic showing a smartphone and the diagnostic attachment for antimicrobial susceptibility testing. (Image credit: Aydogan Ozcan/UCLA)|
The lowest concentration of antibiotic that prevented the growth of bacteria is used to track drug resistance. A criterion—susceptibility to antibiotics or resistance to them—is assigned to each bacteria/drug combination to guide the physician in treatment decisions. A susceptible result indicates that the organisms that have infected the patient should respond to therapy, while a resistant organism will not be inhibited by the concentrations of antibiotic achieved with normal dosages used for that drug.
The researchers, led by Aydogan Ozcan, Chancellor's Professor of Electrical Engineering and Bioengineering at the UCLA Henry Samueli School of Engineering and Applied Science, tested their device in clinical settings at UCLA. They used special plates prepared with 17 different antibiotics targeting Klebsiella pneumoniae, a bacteria containing highly resistant antimicrobial profiles. Using 78 samples from patients, test results showed that the mobile-phone-based reader meets the FDA-defined criteria for laboratory testing, with a detection accuracy of 98.2%.
Full details of the work appear in the journal Scientific Reports; for more information, please visit http://dx.doi.org/10.1038/srep39203.