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

被引:3
作者
Jiang, Xin [1 ]
Borkum, Talya [1 ]
Shprits, Sagi [2 ]
Boen, Joseph [3 ]
Arshavsky-Graham, Sofia [1 ]
Rofman, Baruch [4 ]
Strauss, Merav [5 ]
Colodner, Raul [5 ]
Sulam, Jeremias [3 ]
Halachmi, Sarel [2 ,6 ]
Leonard, Heidi [1 ,7 ]
Segal, Ester [1 ]
机构
[1] Technion Israel Inst Technol, Dept Biotechnol & Food Engn, IL-3200003 Haifa, Israel
[2] Bnai Zion Med Ctr, Dept Urol, IL-3104800 Haifa, Israel
[3] Johns Hopkins Univ, Dept Biomed Engn, Clark 320B, 3400 N Charles St, Baltimore, MD 21218 USA
[4] Technion Israel Inst Technol, Dept Mech Engn, IL-3200003 Haifa, Israel
[5] Emek Med Ctr, Lab Clin Microbiol, IL-1834111 Afula, Israel
[6] Technion Israel Inst Technol, Rappaport Fac Med, IL-3200003 Haifa, Israel
[7] Potomac Photon Inc, 1450 S Rolling Rd, Halethorpe, MD 21227 USA
基金
以色列科学基金会;
关键词
antibiotic resistance; antimicrobial susceptibility testing; bacteria; diffraction gratings; machine learning; optical sensors; urinary tract infecion; BACTERIAL-GROWTH; ANTIBIOTIC SUSCEPTIBILITY; STAPHYLOCOCCUS-AUREUS; VITEK-2; SYSTEM; RESISTANCE; IDENTIFICATION; PLATFORM;
D O I
10.1002/advs.202303285
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
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 & SIM; 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.
引用
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页数:14
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