School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou 215009, China.
School of Chemistry and Chemical Engineering, Southwest University, Chongqing 400715, China.
ACS Sens. 2024 Apr 26;9(4):1945-1956. doi: 10.1021/acssensors.3c02687. Epub 2024 Mar 26.
Urinary tract infections (UTIs), which can lead to pyelonephritis, urosepsis, and even death, are among the most prevalent infectious diseases worldwide, with a notable increase in treatment costs due to the emergence of drug-resistant pathogens. Current diagnostic strategies for UTIs, such as urine culture and flow cytometry, require time-consuming protocols and expensive equipment. We present here a machine learning-assisted colorimetric sensor array based on recognition of ligand-functionalized Fe single-atom nanozymes (SANs) for the identification of microorganisms at the order, genus, and species levels. Colorimetric sensor arrays are built from the SAN Fe-NC functionalized with four types of recognition ligands, generating unique microbial identification fingerprints. By integrating the colorimetric sensor arrays with a trained computational classification model, the platform can identify more than 10 microorganisms in UTI urine samples within 1 h. Diagnostic accuracy of up to 97% was achieved in 60 UTI clinical samples, holding great potential for translation into clinical practice applications.
尿路感染（UTIs）可导致肾盂肾炎、尿脓毒血症，甚至死亡，是全球最普遍的传染病之一。由于耐药病原体的出现，治疗费用显著增加。目前用于尿路感染的诊断策略，如尿液培养和流式细胞术，需要耗时的方案和昂贵的设备。我们在这里提出了一种基于配体功能化 Fe 单原子纳米酶（SAN）识别的机器学习辅助比色传感器阵列，用于鉴定微生物的目、属和种水平。比色传感器阵列由用四种识别配体功能化的 SAN Fe-NC 构建，产生独特的微生物识别指纹。通过将比色传感器阵列与经过训练的计算分类模型集成，该平台可以在 1 小时内识别出 UTI 尿液样本中的 10 多种微生物。在 60 个 UTI 临床样本中，诊断准确率高达 97%，具有转化为临床实践应用的巨大潜力。
