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 Fe₁–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.

中文翻译：

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用于智能快速诊断尿路感染的机器学习辅助比色传感器阵列

尿路感染（UTI）可导致肾盂肾炎、尿脓毒症甚至死亡，是全球最流行的传染病之一，由于耐药病原体的出现，治疗费用显着增加。目前的尿路感染诊断策略，例如尿培养和流式细胞术，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铁单原子纳米酶（SAN）识别的机器学习辅助比色传感器阵列，用于在目、属和种水平上识别微生物。比色传感器阵列由 SAN Fe ₁ –NC 构建而成，具有四种类型的识别配体，可生成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相结合，该平台可以在 1 小时内识别 UTI 尿液样本中的 10 多种微生物。 60 个 UTI 临床样本的诊断准确率高达 97%，具有转化为临床实践应用的巨大潜力。