Nanopore Identification of L-, D-Lactic Acids, D-Glucose and Gluconic Acid in the Serum of Human and Animals.

IF 10.7 2区 材料科学 Q1 CHEMISTRY, PHYSICAL
Small Methods Pub Date : 2025-01-01 Epub Date: 2024-06-12 DOI:10.1002/smtd.202400664
Wendong Jia, Yusheng Ouyang, Shanyu Zhang, Panke Zhang, Shuo Huang
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引用次数: 0

Abstract

DL-Lactic acid and D-glucose are important human health indicators. Their aberrant levels in body fluids may indicate a variety of human pathological conditions, suggesting an urgent need of daily monitoring. However, simultaneous and rapid analysis of DL-lactic acid and D-glucose using a sole but simple sensing system has never been reported. Here, an engineered Mycobacterium smegmatis porin A (MspA) nanopore is used to simultaneously identify DL-lactic acid and D-glucose. Highly distinguishable nanopore event features are reported. Assisted with a custom machine learning algorithm, direct identification of DL-lactic acid and D-glucose is performed with human serum, demonstrating its sensing reliability against complex and heterogeneous samples. This sensing strategy is further applied in the analysis of different animal serum samples, according to which gluconic acid is further identified. The serum samples from different animals report distinguishable levels of DL-lactic acid, D-glucose and gluconic acid, suggesting its potential applications in agricultural science and breeding industry. This sensing strategy is generally direct, rapid, economic and requires only ≈µL of input serum, suitable for point of care testing (POCT) applications.

Abstract Image

人和动物血清中 L-、D-乳酸、D-葡萄糖和葡萄糖酸的纳米孔鉴定。
DL 乳酸和 D 葡萄糖是重要的人体健康指标。它们在体液中的异常水平可能预示着人体的各种病理状况,因此迫切需要进行日常监测。然而,使用唯一但简单的传感系统同时快速分析 DL-乳酸和 D-葡萄糖还从未有过报道。在这里,我们利用一种经改造的分枝杆菌孔蛋白 A(MspA)纳米孔来同时鉴定 DL-乳酸和 D-葡萄糖。报告了高度可区分的纳米孔事件特征。在定制机器学习算法的辅助下,对人血清中的 DL-乳酸和 D-葡萄糖进行了直接识别,证明了其对复杂异质样品的传感可靠性。这一传感策略还被进一步应用于不同动物血清样本的分析,并据此进一步识别葡萄糖酸。不同动物血清样本中的 DL-乳酸、D-葡萄糖和葡萄糖酸含量各不相同,这表明它在农业科学和育种工业中具有潜在的应用价值。这种传感策略通常直接、快速、经济,只需输入≈µL的血清,适用于护理点检测(POCT)应用。
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来源期刊
Small Methods
Small Methods Materials Science-General Materials Science
CiteScore
17.40
自引率
1.60%
发文量
347
期刊介绍: Small Methods is a multidisciplinary journal that publishes groundbreaking research on methods relevant to nano- and microscale research. It welcomes contributions from the fields of materials science, biomedical science, chemistry, and physics, showcasing the latest advancements in experimental techniques. With a notable 2022 Impact Factor of 12.4 (Journal Citation Reports, Clarivate Analytics, 2023), Small Methods is recognized for its significant impact on the scientific community. The online ISSN for Small Methods is 2366-9608.
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