Discipline: Technology and Engineering
Subcategory: Biomedical Engineering
Calvin Brown - UCLA
Co-Author(s): Steve Feng: Electrical & Computer Engineering, UCLA; Derek Tseng: Electrical & Computer Engineering, UCLA; Dino Di Carlo: Bioengineering, UCLA; California NanoSystems Institute, UCLA; Jonsson Comprehensive Cancer Center, UCLA; Omai B. Garner: Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, UCLA; Aydogan Ozcan: Electrical & Computer Engineering, UCLA; Bioengineering, UCLA; California NanoSystems Institute, UCLA; Department of Surgery, David Geffen School of Medicine, UCLA
The effectiveness of antibiotics has decreased over time due to the rise of antibiotic resistance in bacteria. Over-prescription of antibiotics, heavy use in agriculture, and lack of economic incentives to develop new antibiotics have all been blamed for the rise of resistant strains. Antimicrobial susceptibility testing (AST) is used to combat resistance by determining the proper combinations and concentrations of drugs to prescribe each patient. In the gold standard method, broth microdilution, patient isolates are deposited in the wells of a microplate containing antibiotics. After incubation, the wells are visually inspected for turbidity, indicating bacterial growth, the minimum inhibitory concentration (MIC) is determined for each drug, and the medication is prescribed based on the antibiotics’ observed ability to prevent growth.
Drawbacks to broth microdilution include the cost, the need for a diagnostician to manually read and record results, and the time required for growth to become clear to the naked eye. We developed a cost-effective, smartphone-based autonomous AST reader to drastically reduce the cost and labor required for AST . A 3D printed smartphone attachment houses 24 LEDs, AAA batteries, an optical diffuser, the 96-well microplate, 96 optical fibers (one per well), and an external lens. The LEDs and diffuser illuminate the wells from above, and the optical fibers transfer the transmitted light to the smartphone camera, after passing through the external lens to produce the necessary demagnification so that the entire plate can be analyzed from a single image. The user operates the system using a custom app running on the smartphone, which sends images to a local PC or a remote server, where processing is performed, and displays the results to the user in about 1 minute. The cost-effective design of the system and the use of a smartphone to send images to the cloud for processing allows our system to perform portable AST even in resource-limited settings. Our system was validated through testing of 78 clinical isolates of Klebsiella pneumoniae and comparison with the gold standard method. We achieved an average well accuracy of 98.2%, an MIC accuracy of 95.1%, a drug susceptibility accuracy of 99.2%, a very major error rate of 0%, a major error rate of 0.2%, and a minor error rate of 0.7%, which altogether surpass the FDA-defined criteria for AST.
In conclusion, our system is capable of performing cost-effective AST in resource-limited settings, without the need for a trained diagnostician. Because the system relies on an automated optical measurement instead of the trained eye of a professional, future work could enable real-time monitoring and early detection of resistance.
:  S. Feng, D. Tseng, D. D. Carlo, O. B. Garner, and A. Ozcan, ‘High-throughput and automated diagnosis of antimicrobial resistance using a cost-effective cellphone-based micro-plate reader,’ Sci. Rep., vol. 6, p. srep39203, Dec. 2016.
Funder Acknowledgement(s): NSF EFRI and NSF Graduate Research Fellowship Programs are acknowledged.
Faculty Advisor: Aydogan Ozcan, firstname.lastname@example.org
Role: As a new graduate student on this project, I have been contributing to smart system design and engineering for antimicrobial susceptibility testing using computational photonics.