Better explicating the strengths and shortcomings of these models will help refine projections and improve transparency in the years ahead.
Image credit: Witsawat.S.
Edited by Robert Langer, Massachusetts Institute of Technology, Cambridge, MA, and approved June 1, 2017 (received for review March 6, 2017)
Significance
Antibiotic resistance is fueled by antibiotic misuse and has become a major global health concern. The phenomenon warrants improved diagnostics that can more rapidly and efficiently elucidate information about the infectious agent to aid in establishing a more targeted and knowledge-based treatment regimen. This paper introduces a rapid antibiotic susceptibility test and automated data analysis algorithm that can, unlike traditional methods, deliver results on the same working day in an efficient and translatable manner for clinical use. This paper also introduces a method for direct urine testing that can help save days of diagnosis time. The platform is expected to promote more judicious use of antibiotics, thereby reducing the emergence of antibiotic resistance, lowering healthcare costs and ultimately saving lives.
Abstract
Antibiotic resistance is a major global health concern that requires action across all sectors of society. In particular, to allow conservative and effective use of antibiotics clinical settings require better diagnostic tools that provide rapid determination of antimicrobial susceptibility. We present a method for rapid and scalable antimicrobial susceptibility testing using stationary nanoliter droplet arrays that is capable of delivering results in approximately half the time of conventional methods, allowing its results to be used the same working day. In addition, we present an algorithm for automated data analysis and a multiplexing system promoting practicality and translatability for clinical settings. We test the efficacy of our approach on numerous clinical isolates and demonstrate a 2-d reduction in diagnostic time when testing bacteria isolated directly from urine samples.
Footnotes
- ↵1To whom correspondence should be addressed. Email: shulamit{at}bm.technion.ac.il.
Author contributions: J.A., D.R., T.B.A., Y.G., M.B., and S.L. designed research; J.A. and D.R. performed research; M.T.-R. contributed new reagents/analytic tools; J.A. analyzed data; J.A., D.R., T.B.A., Y.G., M.B., and S.L. wrote the paper; J.A. developed the analysis algorithm; T.B.A. helped design the analysis algorithm; and Y.G. guided the clinical work.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1703736114/-/DCSupplemental.
Rapid phenotypic AST using nanoliter arrays
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