DOI: 10.1002/advs.202303285 ISSN:

Accurate Prediction of Antimicrobial Susceptibility for Point‐of‐Care Testing of Urine in Less than 90 Minutes via iPRISM Cassettes

Xin Jiang, Talya Borkum, Sagi Shprits, Joseph Boen, Sofia Arshavsky‐Graham, Baruch Rofman, Merav Strauss, Raul Colodner, Jeremias Sulam, Sarel Halachmi, Heidi Leonard, Ester Segal
  • General Physics and Astronomy
  • General Engineering
  • Biochemistry, Genetics and Molecular Biology (miscellaneous)
  • General Materials Science
  • General Chemical Engineering
  • Medicine (miscellaneous)


The extensive and improper use of antibiotics has led to a dramatic increase in the frequency of antibiotic resistance among human pathogens, complicating infectious disease treatments. In this work, a method for rapid antimicrobial susceptibility testing (AST) is presented using microstructured silicon diffraction gratings integrated into prototype devices, which enhance bacteria‐surface interactions and promote bacterial colonization. The silicon microstructures act also as optical sensors for monitoring bacterial growth upon exposure to antibiotics in a real‐time and label‐free manner via intensity‐based phase‐shift reflectometric interference spectroscopic measurements (iPRISM). Rapid AST using clinical isolates of Escherichia coli (E. coli) from urine is established and the assay is applied directly on unprocessed urine samples from urinary tract infection patients. When coupled with a machine learning algorithm trained on clinical samples, the iPRISM AST is able to predict the resistance or susceptibility of a new clinical sample with an Area Under the Receiver Operating Characteristic curve (AUC) of ∼ 0.85 in 1 h, and AUC > 0.9 in 90 min, when compared to state‐of‐the‐art automated AST methods used in the clinic while being an order of magnitude faster.

More from our Archive