State Key Laboratory of Fluid Power and Mechatronic Systems, College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, People's Republic of China.
Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Sir Run Run Shaw Hospital, Zhejiang University, Hangzhou 310016, People's Republic of China.
ACS Nano. 2023 Mar 14;17(5):4551-4563. doi: 10.1021/acsnano.2c10584. Epub 2023 Mar 3.
Antibiotic-resistant ESKAPE pathogens cause nosocomial infections that lead to huge morbidity and mortality worldwide. Rapid identification of antibiotic resistance is vital for the prevention and control of nosocomial infections. However, current techniques like genotype identification and antibiotic susceptibility testing are generally time-consuming and require large-scale equipment. Herein, we develop a rapid, facile, and sensitive technique to determine the antibiotic resistance phenotype among ESKAPE pathogens through plasmonic nanosensors and machine learning. Key to this technique is the plasmonic sensor array that contains gold nanoparticles functionalized with peptides differing in hydrophobicity and surface charge. The plasmonic nanosensors can interact with pathogens to generate bacterial fingerprints that alter the surface plasmon resonance (SPR) spectra of nanoparticles. In combination with machine learning, it enables the identification of antibiotic resistance among 12 ESKAPE pathogens in less than 20 min with an overall accuracy of 89.74%. This machine-learning-based approach allows for the identification of antibiotic-resistant pathogens from patients and holds great promise as a clinical tool for biomedical diagnosis.
耐药性 ESKAPE 病原体引起的医院感染在全球范围内导致了巨大的发病率和死亡率。快速鉴定抗生素耐药性对于预防和控制医院感染至关重要。然而，目前的技术，如基因型鉴定和抗生素药敏试验，通常耗时且需要大规模的设备。在这里，我们通过等离子体纳米传感器和机器学习开发了一种快速、简便、灵敏的技术，用于确定 ESKAPE 病原体中的抗生素耐药表型。这项技术的关键是包含金纳米粒子的等离子体传感器阵列，这些金纳米粒子用疏水性和表面电荷不同的肽进行了功能化。等离子体纳米传感器可以与病原体相互作用，产生改变纳米粒子表面等离子体共振（SPR）光谱的细菌指纹。结合机器学习，可以在不到 20 分钟的时间内识别 12 种 ESKAPE 病原体中的抗生素耐药性，总体准确率为 89.74%。这种基于机器学习的方法可以从患者中识别出耐药性病原体，有望成为生物医学诊断的临床工具。
